Technical Guide for the Implementation of a Regional Climate Information System Applied to the Agricultural Risk Management in the Andean Countries

TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO THE AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES

PROJECT IADB ATN/OC – 10064 – RG

Funded by: Inter-American Development Bank Regional Public Goods

Executing Agency:

INTERNATIONAL RESEARCH CENTER ON “EL NIÑO”

Escobedo #1204 y 9 de Octubre Edificio Fundación El Universo, 1er piso Phone: (593 4) 2514770 Fax: (593 4) 2514771 P.O. Box # 09014237 Guayaquil-Ecuador

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 104 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES

International Research Center on “El Niño” (2009) PROJECT IADB ATN/OC – 10064 – RG

It is allowed to reproduce and communicate this guide as long as the source is referenced correctly and it is not used for commercial purposes.

Some copyrights http://creativecommons.org/licenses/by-nc/3.0/ Funded by:

Concept, Design & Infographics Inter-American Development Bank 2009, by Leonardo Briones Rojas

Title page Executing Agency: José Benito Valarezo Loor

Photo Abigail Alvarado, Patricio López, Borja Santos

Print Gráficas Hernández Cía. Ltda. Cuenca, Ecuador December, 2009

To refer the whole Technical Guide: Martínez, R., Mascarenhas, A., Alvarado, A., (ed)., 2009. Technical Guide for the Implementation of a Regional Climate Information System Applied to the Agricultural Risk Management in the Andean Countries. International Research Center on “El Niño” –CIIFEN, p 1-160. INTERNATIONAL RESEARCH CENTRE ON EL NIÑO

To refer one chapter of the Technical Guide: Ycaza P., Manobanda N., 2009. Implementation of Agro-climatic Risk Maps, p 50-62. On the Technical And Guide for the Implementation of a Regional Climate Information System Applied to the Agricultural Risk Management in the Andean Countries., Martínez, R., Mascarenhas, A., Alvarado, A., (ed)., 2009. Interna- The National Meteorological and Hydrological Services of tional Research Center on “El Niño” –CIIFEN, p 1-160. Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela

ISBN: 978-9978-9934-1-5

This publication was made by the International Research Center for “El Niño” –CIIFEN under the Project ATN/OC 10064-RG “Climatic information Applied to Risk Management in Andean Countries”, funded by the Inter-American Development Bank, IDB, under the initiative of Regional Public Goods (2006).

103 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES OCTOBER, 2009 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTURAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES

PROJECT IADB ATN/OC – 10064 – RG

Funded by: Inter-American Development Bank

Executing Agency:

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO

And The National Meteorological and Hydrological Services of Bolivia, Chile, Colombia, Ecuador, Peru and Venezuela

OCTOBER, 2009 Editorial Team:

Rodney Martínez Güingla Affonso Da Silveira Mascarenhas Jr. Abigail Alvarado Almeida

Project Team:

General Coordinator Rodney Martínez Güingla

Financial Administration and Acquisitions Systems Engineering Roma Lalama Franco Katiusca Briones Estebanez

Information Assistant Risk Maps Nadia Manobanda Herrera Harold Troya Pasquel

Regional Agriculturist Risk Climatical Data Processing Angel Llerena Hidalgo Pilar Cornejo Rodriguez

Local Risk Experts Local Experts on Information Management Bolivia Silvia Coca Uzuna Bolivia Javier Caba Olguín Chile Claudio Fernandez Pino Chile Miguel Egaña Colombia José Boshell Villamarín Colombia Juan Gómez Blanco Ecuador Emilio Comte Saltos Ecuador Emilio Comte Saltos Perú Oscar Quincho Ramos Perú Juan Ramos Escate Venezuela Pedro Rodriguez González Venezuela Pedro Rodriguez González

Data Digitalization Statistical Modeling Bolivia José Valeriano Maldonado Marco Paredes Riveros Luis Bustillos Paz Chile Viviana Urbina Guerrero Numerical Modeling Patricia Berrios Leiva Ángel Muñoz Solórzano Colombia Carlos Torres Triana Paola Bulla Portuguez Numerical Modeling Ecuador Carlos Naranjo Silva Ricardo Marcelo Da Silva Ana Zambrano Vera Perú Luis Zevallos Carhuaz Virtual Core Juan Bazo Zambrano Red de Universidades del Eje Cafetero Alma Mater Venezuela Vickmary Nuñez Oropeza Gabriel Diaz Loreto Data Base Centro de Tecnologías de la Información - ESPOL

CIIFEN Staff – Project Counterpart

International Director International Director Patricio López Carmona Affonso Da Silveira Mascarenhas Jr. 2006-2007 2008-2009

Geographic Information Systems Research Systems Data and Products Pilar Ycaza Olvera Abigail Alvarado Almeida Management Mishell Herrera Cevallos Alexandra Rivadeneira Uyaguari Juan José Nieto López Carlos Meza Baque Carlos Zambrano Alcívar

Administration Informatic Support Mayra Mayorga López Alberto Abad Eras Evelyn Ortíz Sánchez Victor Hugo Larrea Alvarado NATIONAL METEOROLOGICAL SERVICES

BOLIVIA METEOROLOGICAL AND HYDROLOGICAL NATIONAL SERVICE - SENAMHI www.senamhi.gov.bo

Director Focal Point for the Project Carlos Díaz Escobar Pablo Elmer

Statistical Modeling Dynamic Modeling Agro-Climatic Gualberto Carrasco Gualberto Carrasco Risk Maps Yaruska Castellón Erick Pereyra Yaruska Castellón Nidia Zambrano Ramiro Solíz Oscar Puita Virginia Rocha

CHILE METEOROLOGICAL DIRECTION OF CHILE - DMC www.meteochile.cl

Director Focal Point for the Project Agro-Climatic Risk Maps Myrna Araneda Fuentes Gualterio Hugo Ogaz Patricio Lucabeche José Curihuinca

Statistical Modeling Dynamic Modeling Information Systems Juan Quintana Claudia Villarroel Miguel Egaña Roberto Hernández

COLOMBIA INSTITUTE FOR HYDROLOGICAL, METEOROLOGICAL www.ideam.gov.co AND ENVIRONMENTAL STUDIES - IDEAM

Director Focal Point for the Project Agro-Meteorological Analysis Carlos Costa Ernesto Rangel Mantilla Gonzalo Hurtado Moreno Ricardo Lozano Christian Euscátegui Ruth Mayorga Márquez

Modeling Quality Analysis Gloria León Aristizábal Ruth Correa Amaya NATIONAL METEOROLOGICAL SERVICES

ECUADOR NATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY - INAMHI www.inamhi.gov.ec

Director Focal Point for the Project Agro-Climatic Risk Maps Carlos Lugo Raúl Mejía Fanny Friend Flavio Ramos

Statistical Modeling Dynamic Modeling Cristina Recalde Jaime Cadena

PERÚ NATIONAL SERVICE OF METEOROLOGY AND HYDROLOGY - SENAMHI www.senamhi.gob.pe

Director Focal Point for the Project Wilar Gamarra Molina Darío Fierro Constantino Alarcón

Statistical & Dynamic Information Systems Agro-Climatic Risk Maps Modeling Luis Zevallos Carhuaz Darío Fierro Zapata Carmen Reyes Bravo Kevin Sánchez Zavaleta Juan Bazo Zambrano Nelly Perez Díaz

METEOROLOGICAL SERVICE OF VENEZUELA www.meteorologia.mil THE BOLIVARIAN NATIONAL AVIATION

Director Focal Point for the Project Ramón Alexander Quintero Velásquez Araguayan

Statistical & Dynamic Data Systems Agro-Climatic Risk Maps Modeling and Digitalizing Carlos Ojeda Luis Monterrey Richard Núñez Luis Monterrey Alexandra Mata Jenny Castillo César Yauca Elddy Anselmi Manuel González INTRODUCTION

ince it was established in January 2003, one of the most important mandates of the International Research Center on SEl Niño (CIIFEN) has been to build the necessary bridges between the climate information suppliers and the users from other sectors of the society.

The ultimate goal is to use the benefits provided by earth observation, science and predictions in order to allow our society to live better. When we talk about risk management, it results in reduced loss of life and goods for development support.

To review all the climate information to make it a tool for human welfare is not easy, it requires a holistic vision, inter-and trans-disciplinary dialogue and, above all, it requires breaking several paradigms.

In 2003 the World Meteorological Organization, through its Division of Services and Climate Applica- tions, organized, in alliance with CIIFEN, a regional workshop to identify the needs of climate information for the agricultural sector. This meeting provided us with essential information to generate (after several years) a regional proposal that addresses the needs of this important sector.

In 2006, the Inter-American Development Bank (IADB), under the modality of Regional Public Goods, approved the project entitled “Climate Information Applied to Agricultural Risk Management in the Andean countries” to be carried out by CIIFEN and the National Meteorological Services of Boli- via, Chile, Colombia, Ecuador, Peru and Venezuela.

After three years of efforts, regional cooperation and with the trust and support of the IADB, we can bear witness to this important initiative through this Technical Guide, which describes step by step how we implemented the system in each one of its components, including the learned lessons, sustainability strategies and future challenges.

With deep gratitude to the Inter-American Development Bank (IADB)The World Meteorological Orga- nization (WMO), The National Meteorological and Hydrological Services (NMHSs) and the Internatio- nal Research Centre on El Niño (CIIFEN), we present the “Technical Guide for the Implementation of a Regional Climate Information System Applied to Agriculture Risk Management in the Andean Countries”. We hope that it can be replicated in other places around the world for the benefit of our society.

Dr. Affonso Mascarenhas Oc. Rodney Martinez Güingla International Director Project Coordinator CIIFEN ATN/OC 10064-RG

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 107 INDEX

Introduction 107

Chapter I: Implementation of the Virtual Core for Climate Applications 111 (VCCA)

1.1 Conceptual model 112

1.2 Technologic Platform 112

1.2.1 VCCA Architecture 112 1.2.2 Physical Infrastructure 112 1.2.3 Logical Infrastructure 112

1.3 Applications That make up the VCCA 113

1.3.1 Regional Climate Data Base 113 1.3.2 Map Server 114 1.3.3 Display of Climate Model Products 115 1.3.4 Virtual Library 115 116 1.4 Implementation Process of Regional Climate Data Base 117 Chapter II: Implementation of statistical modeling for climate prediction 118 2.1 Conceptual and methodological elements 119 2.2 Management to update the information of the Predicting Variables 119 2.2.1 How to perform the Alternative Method for Forecast Updates. 119 2.2.1.1. Procedure for obtaining the sea surface temperature (SST) variable. 121 2.2.1.2 Procedure for obtaining the wind variable at altitude, geo-potential, temperature at mandatory levels. 122 2.3 Operating simultaneous predictors with CPT. 122 2.3.1 Climate Forecasting with Simultaneous Predictors 123 2.4 Decision criteria for managing the CPT results. 127 2.5 Considerations for the interpretation of terciles 127 2.6 FAQs related to CPT operation. 131 Chapter III: Implementation of numerical models for climate prediction 132 3.1 Step by step installation and implementation procedures for MM5 and WRF models in Climate Mode. 132 3.1.1 Operating System 132 3.1.2 Atmospheric Models 132 3.1.2.1 MM5 137 3.1.2.2 CMM5 137 3.1.2.3 WRF 139 3.1.2.4 CWRF

108 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES 3.1.3 Oceanographic Models 140

3.1.3.1 ROMS 140

3.1.4 Displayers 141

3.1.4.1 GRADS 141 3.1.4.2 Vis5D 141

3.2 Implementation of Numerical Modeling for Climate Forecasting 143 The Regional Group of Numerical Modeling

Chapter IV: Implementation of Agro-Climatic Risk Maps 145

4.1 Definition of Risk 146

4.2 Conceptual Mathematical Model of Agro-Climatic risk 146

4.3 Components and Variables of Agro-Climatic Risk 146

4.3.1 Thread 147 4.3.2 Vulnerability 147

4.4 Project Application Areas 148

4.5 Information Requirements 150

4.5.1 Agro-ecological 150 4.5.2 Base mapping 150 4.5.3 Thematic mapping 150 4.5.4 Treatment of information 150 4.5.5 Soil and climate characteristics in pilot areas 150

4.6. Agro-Climatic Risk Calculation 152

4.7. Agro-Cimatic risk in the Andean Countries 154

Chapter V: Implementation of local systems of climate information 161

5.1 Conceptual and methodological elements. 162

5.2 Identification and mapping of key components. 163

5.3 Strategic Alliances 164

5.4 Strategic alliances with local authorities. 165

5.5 Strategic alliances with the private sector. 165

5.5.1 Journals Specialized in Agriculture 166 5.5.2 Cell phone Company 167

5.6 Strategic alliances with the media. 168

5.7 Training Strategies 169

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 109 Chapter VI: Capacity building in Western South America 173

6.1 Regional Training Workshop on Climate Modeling statistics 174

6.2 Regional Training Workshop on Numerical Modeling for Climate predictors 174

6.3 Regional Training Workshop for Agro-Climatic Risk Mapping 174

6.4 Regional Workshop on Numerical Modeling of Weather and Climate II 175

6.5 International Training Workshop on Climate Data Processing 175

Chapter VII: Performance Indicators. 177

Chapter VIII: Learned Lessons 181

Chapter IX: Future Actions 185

Chapter X: Elements of Sustainability 187

Bibliographic References 189

110 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES CHAPTER I Implementation of the Virtual Core for Climate Applications (VCCA) Katiusca Briones CHAPTER I [email protected]

1.1 CONCEPTUAL MODEL 1.2.3 Logical Infrastructure

The Virtual Core for Climate Applications (VCCA) consists The client-server architecture requires a server capable of of a basic computing infrastructure to run climate applica- conducting central processing of the applications running tions designed to provide climate information through an on it, while clients “ask for” information without having to easy to use and easy access format through the Internet. process it internally. On this logic infrastructure scheme, a centralized databases is created according to the applica- Under this philosophy, the VCCA centralizes all the neces- tion, so the information is kept in one place with the partic- sary functionalities for different types of advanced WEB ap- ularity of being accessible for viewing and/or maintenance, plications, such as: products presentation, users’ control, depending on the type of user (end user or administrator). management of geographical information system, bibliographic information and search for information. The developed applications communicate with the corresponding database independently, placing the visual 1.2 TECHNOLOGIC PLATFORM interface over the one that is displaying the information requested by the user (Fig. 2). 1.2.1 VCCA Architecture

The main purpose of the applications running on the VCCA is to provide information to end users, without requiring the installation of any special software. This established the client-server architecture, in which CIIFEN would be in charge of the central server, and the end users would have access through web interface using the Internet. This allows simultaneous user connectivity and protection of published information.

Figure 1 shows VCCA architecture graphically, in which the physical infrastructure (servers), the logic infrastructure (software) and the end users intervene.

Figure 2. Software Infrastructure of Virtual Core for Clima- te Applications (VCCA)

The technical details of the software used in the VCCA are described as follows:

Operating Systems Management and applications servers, run on Linux SUSE Operating System V.10, which has been demonstrated to be sufficiently stable, ensuring the availability of permanent climate applications over time.

Database Management System (DMS) Database Management Systems DMS are intended to support the tasks of defining, creating and manipulating re- Figure 1. Virtual Core for Climate Applications Architec- lational databases; for which that allows for features such ture (VCCA) as: concurrency control, data backup methods and access control applying user profiles. 1.2.2 Physical infrastructure The virtual core operates with the DMS, called PostgreSQL The VCCA was implemented with two servers, for the ad- 8.3; this is a Relational Object type system, and is used ministration of CIIFEN’s internal network and VCCA instal- extensively due to the characteristics of the standards ap- lation. The servers used are: Dell PowerEdge 2950 with a plied, the securities and the capability of communicating Xeon Dual Core 2.66GHz processor with 4GB of memory with various types of applications, among which is the abil- and a disk capacity of 600GB (primary server), 300GB (sec- ity to store spatial data, which is needed for applications of ondary server) and RAC type servers. the geographical information systems. Spatial Information Management System (SIMS) The web visualization of cartographic information, agricul-

112 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R I ture climate risk and geographic information system re- tem applied to agriculture climate risk management in the quires the use of several special-purpose tools that togeth- Andean countries as a Regional Public Good that contrib- er allows the full functionality of the map display system. utes to the understanding of past climate and its important The tools used are: role of evolution over time.

PostGIS: Modules under GNU license provide the Post- Figure 3 shows the Database which is available at http://vac. greSQL Database management system with the ability to ciifen-int.org and contains records from 170 meteorological manage spatial information. stations from 1952 until 2008 and is the start of a data ex- change system without precedent, which will also improve MapServer 5: CGI Aplication (Common Gateway Interface) climate forecasting services in the region. It contains daily which is a standard for communicating between a Web records of Precipitation, Maximum Temperature, Mini- server and a program, so that the user can interact through mum Temperature, Basic Data Stations and also displays the Internet (in the case of dynamic maps). climate products as time series or spatial graphics.

Grass: Geographical Information System, that handles the information on the web.

Web Server Apache 2.0: HTTP SERVER (the protocol that defines the semantics used for clients and servers to communicate with each other); it is multi-platform open code. Its architecture allows the addition of modules to provide several functions, such as dynamic webpage support and message encryp- tion.

Application Support Java Application Platform (SDK): Platform on which certain components (climate database) of the VCCA are run.

Perl: Program to run certain components of the applications (display of numerical modeling products). Figure 3. Startup Screen of the Regional Climate Database

End User The application allows to view different types of graphs One of the goals outlined in the development of VCCA, (time series, contour plots, histograms) and to check me- was to eliminate the need for the user to install any spe- teorological stations (location, general information). For cial software. To access any of VCCA applications, the user the creation of the Databse and its updating, a Protocol needs only to have an Internet connection, run his preferred between National Meteorological Services and CIIFEN was browser program and access the appropriate link. signed (Annex I).

1.3 APPLICATIONS THAT MAKE UP THE VCCA Available chart types: The application provides three sets of information: The project developed the applications in the VCCA: • Regional Climate Data Base: http://vac.ciifen-int.org Data Search: allows us to select the graphic display of time • Map Server: http://ac.ciifen-int.org/sig-agroclimatico series and histograms, and also to download the data in • Display of Climate Modeling Products: text format of the CPT model1 in maximum / minimum / http://ac.ciifen-int.org/modelos accumulated monthly, bimonthly, quarterly or yearly. (Fig. • Virtual Library: http://ac.ciifen-int.org/biblioteca/ 4) (Fig. 5)

1.3.1 Regional Climate Data Base

The Regional Climate Data Base corresponds to an application for displaying climate data of temperature and precipitation in the Andean countries (Bolivia, Chile, Colom- bia, Ecuador, Peru and Venezuela).

The Regional Climate Data Base for Western South Ameri- ca, is an cooperative effort without precedents among the National Meteorological Services of the region and is a gi- ant step towards the integration of climate data to be used use in regional forecasting and also to contribute to atmospheric sciences research.

This information resource is made possible thanks to the unflinching support and hard work of the NMHSs of Bolivia, Figure 4. Screen of Data Search by Regional Climate Chile, Colombia, Ecuador, Peru and Venezuela. The data- Database Stations base is one of the columns of the climate information sys- 1. Climate Prediction Tool, http://portal.iri.columbia.edu

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 113 C H A P T E R I

Figure 8. Graphics screen of Climate Products of Regional Climatic Database by country

Figure 5. Display Screen of Time Series of the Regional Data Update Climate Database The Climate Database application is fully upgradeable; to allow this it has an administrator interface, in which each Stations: Displays a list of all the weather stations involved country can connect through the same interface and up- in the ATN / OC 10064-RG project, identifying details of its load the data files. basic information, location and additional information for each of these. (Figure 6) 1.3.2 Map Server

The Map Server Application aims to provide the user with the ability to visually manipulate different levels of GIS information through a friendly web interface, without running any specialized software in the computer.

This Web-based interface provides the ability to view any final product of a GIS, as is the case of the agriculture climate Risk GIS, the initial product placed on the display. We need to indicate that a pre-processing of the levels is necessary in order to publish them from shape to xml format.

Graphic Interface The graphical interface of the Map Server allows end user to select the different Andean countries involved in the project. For each one of them, available information is dis- Figure 6. Detail display screen of the Regional Climate played. (Figure 9). Database Stations

Climate Products: Displays spatial graphics using a format of isolines for precipitation and temperature, in which is possible to select an area of a country or of the South American Continent (Figure 7) (Figure 8).

Figure 9. Map Server Startup Screen

Each link within countries displays a list of layers and topics developed in the project. The selected layers and selected topics are displayed in a GIS management interface, in which the user can hide/display layers, zoom in/out, display Figure 7. Selection screen of Climatic Products of the information from the components of each layer, select com- Regional Climate Database ponents, measuring tools, insertion of points of interest,

114 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R I and download images in GeoTIFF format (geo-referenced image). (Figure 10)

The application has been developed for the user to load new layers of information, for which it is necessary to trans- form each layer from shape format to xml format.

Figure 12. Display screen of climate model products, Accumulated Precipitation variable

Figure 10. Map download tool in GeoTIFF format from the geographic information system based on the Web.

1.3.3 Display of Climate Model Products

The goal of the Climate Modeling Products display is to create an application on which the end user can display products from different numerical climate models (Fig 11).

Figure 13. Display Screen Climate Model Products, Air Temperature variable

since its creation, the goal of the application is to publish books, magazines, reports, presentations, CDs, and more free access information sources and to disseminate it to the general public. (Figure 14)

Figure 11. Home Screen Numerical Modeling Products Display

The developed Web interface allows the user to choose the climate model he wants to view and select the dates on which forecasts have been made. Once the user has chosen the date, he can select the domain and the climate variable, and then, the corresponding product will be displayed. (Figure 12)

The interface on which numerical modeling products are published is Google Earth satellite images interface, which Figure 14. Startup screen Digital Library makes this application a topographic information tool that is allows to visualize areas of different altitudes when ana- The virtual library is published at: http://ac.ciifen-int.org/ lyzing climate forecasts. (Figure 13) biblioteca. There are two search options: by Books and Digital Archives: 1.3.4 Virtual Library Books Section The purpose of the Virtual Library is the systematization of • Contains information of books, magazines, journals, at- the vast amount of information that CIIFEN has collected lases, and other paper publications.

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 115 C H A P T E R I

Digital Archives Section each country can access using the appropriate username • Contains information of presentations, CDs and DVDs of and password to manage their data and also to add new applications, reports, data and projects which CIIFEN has information. compiled from the various events in which it has participat- ed. These archives are available for free. • The Regional Climate Database contains 3,876,035 climate records and will maintain up to date according the The application has a search interface in which the user can established protocol. enter keywords and select the search type. As a result, it will show all matches found in the library, identifying the information of each publication. (Figure 15)

Figure 15. Display screen Digital Library publications

Administrative interface The application has an administrative interface through the Control Panel option, in which the library administrator has several management options, such as adding categories, add/edit/delete/reserve publications and add/delete users.

1.4 IMPLEMENTATION PROCESS OF A RE- GIONAL CLIMATE DATA BASE

Integrating climate information from the Andean countries was a fully coordinated joint effort in which the NMSs provided maximum collaboration to the compilation of national databases. This process was executed in five stages:

• Collection of information: In order to determine the availability of information in different existing formats within each National Weather Service, they proceeded to perform a survey on the staff, which determined the amount of digital data and hard copy information

• Acquisition of computer equipment: The digitization of information required the acquisition of computer equipment; two computers for each NMHS were designated for this purpose.

• Hiring of operators: The amount of information to be loaded was based on surveys, and it was coordinated with each NMS to hire two operators for each institution. They processed the information in the appropriate formats.

• Compilation of information: The digital information was added to the databases of each NMS, increasing the den- sity of climate data in each institution.

• WEB Application Development: Based on information gathered by each NMS, the Web application was developed with the data of precipitation, maximum and minimum temperature. In the application, a database maintenance module was developed in which a representative of

116 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES CHAPTER II Implementation of statistical modeling for climate prediction Marco Paredes CHAPTER II [email protected]

2.1 CONCEPTUAL AND METHODOLOGICAL The Sea Surface Temperature (SST), due to variable inertia, ELEMENTS experiences slowly changes in its physical patterns. Under this premise, any change in the next four or five days will The tool used for the implementation of statistical mod- not be significant on the monthly average, which is carried eling in every country was the Climate Predictability Tool out by averaging the first 27 days of the current month and (CPT), developed by the IRI. The flow chart of actions de- is attached to the time series of sea surface temperature signed for the region is explained in the Figure16. that can be obtained from NOAA/NCD/ERSST. At the end a complete and updated historical series is obtained, which serves as the final predictor.

Development of Atmospheric variables, such as zonal wind, southern wind, temperature at high levels, specific humidity, among oth- CPT Data ers, we must take with extreme caution the changes in these last five days of the month. They can be significant and may modify the average. It is therefore advisable to monitor climatic conditions globally and in particular South Regionalization America or the region of interest. of variables The analysis of the monitoring of various oceanic and atmospheric variables should be conducted every two weeks. If Climate possible, it is recommended to do it on a weekly basis, as monitoring shown in Figure 17:

Updating of predictors

Regionalization of predictor

Run of CPT

Analysis of results

Assemblies

Spatial Graphics

Dissemination

Figure 16. Process for the realization of the seasonal forecast, using the CPT Figure 17: Pressure Monitoring conducted at sea level by NOAA / NCEP / NCAR For the purpose of using the CPT, the information from the National Meteorological Services is collected on the 30th of It shows the average to date. In the right corner it shows each month (28th in February), before the update done by the anomalies that occurred during that period and at the the Global Forecast Center. This will be basic information bottom the climate for the same period, to compare them. that serves as a predictor, under the following assumptions: Likewise, we should keep in mind the monitoring of SST

118 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II and its anomalies and their influence on changes in climate patterns.

2.2 MANAGEMENT INFORMATION UP TO DATE FOR VARIABLE PREDICTORS

2.2.1 How to perform the Alternative Method for Forecast Updates

The monthly predictive indices of global forecasting centers provide information that is available in the IRI’s Data Library. They are updated on the 10th of each month with information from the previous month. As a result, the statis- Figure 19 th tical model run are delayed until a date after the 10 for SST Press the weekly data (weekly): (Figure 20). and the 15th for other predictors.

To avoid this we have chosen an action that allows an update a few days in advance of some of the necessary predictors, especially sea surface temperature (SST) under the following circumstances:

• It is considered that the predictor to be analyzed will not undergo significant changes when it is completed with the missing data until the end of the month. Figure 20 • 75% of the days of the month are averaged so as to be considered representative. This means that at least 21 days Choose the option Sea Surface Temperature (SST) (Figure of the month must have already passed. 21). • The changes in the SST’s values have no abrupt behavior because of the ocean’s inertia (specific heat, which allows a delay in heat loss by up to 5 times longer than on land).

2.2.1.1 Procedure for obtaining the sea surface temperature (SST) variable

In this case, there must be prior historical information from the SST variable obtained from NOAA / NCDC / ERSST in order to predict the “Y” variable. Figure 21 For weekly SST data, the corresponding search in the IRI Choose to download the data of the weeks of interest (Fig- data library is performed in the Air-Sea interface category. ure 22). (Figure 18).

Figure 22 The process is the same that was used to download information on any other variable. The only difference occurs in the window Time (symbolized by the letter T). It places the weeks of the month, considering that it begins Sunday and ends on a Saturday, for example, for the weeks of February are: (Figure 23)

Figure 18. Air-Sea interface Data in the IRI Data Library.

The data belongs to the NOAA / NCEP / EMC global CMB, Reynolds Smith research center. It should be looked for weekly data by entering to version 2 of the Reynolds data (Reyn Smith IOv2). (Figure 19) Figure 23

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 119 C H A P T E R II

You get 03 sets of data (03 weeks); therefore you should perform filtering of information with an average time (called T in CPT), which shows the average of 03 weeks elapsed, corresponding in this example to February. From this part, there are two ways to standardize the resolution between the two data sets from different research centers, which are detailed below:

First way: (Figure 24)

Figure 26 Then, enter on page 2 of the same file and save as text file (which is already transformed). (Figure 27)

Figure 24 Figure 27

Download the monthly average data. The format obtained is the following: (Figure 28)

Note: Keep the following in mind before the process: the resolution of the Reynolds data is 1° x 1° and is not com- patible with the data of CPT when it runs (Source: NOAA / NDCD / ERSST whose resolution is 2° x 2°). To solve this incompatibility a spreadsheet has been compiled (called transformation) which converts the Reynolds data to ERSST data.

The chart below shows the format obtained through the process described above, where the first line and first column indicate resolutions in longitude and latitude respec- tively (1° x 1°). (Figure 25)

Figure 28

Save it to add it to the February predictor history, with a single copy. (Figure 29)

Figure 25 PASTED FILE Copy from the second line all the obtained information in (Weekly averages in the interest month) the file and take it to the spreadsheet 1 of the file TRANS- FORMATION, on the yellow background area (copy and paste), leaving the first row empty. (Figure 26). Figure 29 Then record it.

120 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II

Note that will have information, it starts in one year 1960 and ends with the year 2008, which is ready for inclusion on the CPT. (Figure 30)

Look for resolution changes in first row and column: 2º x 2º. Delete date dashes by Delete date dashes by blank spaces blank spaces

Figure 30

Figure 32 Second way:

Login to expert mode after obtaining the average week (found with a resolution of 1° x 1°), perform the following commands.

X 0 2 358 GRID

Y -88 2 88 GRID

Then download it, save it and paste it on the base obtained from the initial historical series (procedure described in item 2.3.1.1), which is ready for use as a predictor.

2.2.1.2 Procedure for obtaining the wind variable at altitude, geo-potential and temperature at mandatory levels Figure 33 If is the case to work with an atmospheric variable at altitude, there is a practical procedure to work with averages of days elapsed. An option is to use information NOAA Choose the daily data (DAILY) and subsequently the IN- NCEP-NCAR CDAS-1 which lies within the model simula- TRINSIC mode. (Figure 34) tions (HISTORICAL MODEL SIMULATIONS). (Figure 31 and 32).

Figure 34 Figure 31 When information regarding altitude level is required, choose the Pressure Level option, which lets you choose It is necessary to previously have monthly historical data the level of interest. of the variable of interest from the same research center (NOAA NCEP-NCAR CDAS-1) so they have the same reso- Variables that can be provided are multiple; however, the lution in order to fit together more easily. (Figure 33) most common are: geo-potential height, zonal wind, south wind and temperature. They can be seen in the following screen (Figure 35)

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 121 C H A P T E R II

2.3 OPERATING SIMULTANEOUS PREDIC- TORS WITH CPT

2.3.1 Climate Forecasting with Simultaneous Predictors

The run with each predictor must be done individually to obtain the weights that influence the variable to be predicted with some additional comments.

Place the maximum number of modes for the variable X, which corresponds to fewest number obtained between the number of years in historical series and the number of grid points or seasons. The maximum number of variable X is required, so you take 43 (according to the example), although in reality the maximum number for X is 44 (n final - initial n + 1), only that the ordered common pair 1965-2007 Figure 35 is considered so there is an additional process for calculating the weight of 2008, which is described below (Figure The available levels in the library are 1000, 925, 850, 700, 38). 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20 and 10 mb. (Figure 36)

Figure 38

You should obtain from the menu FILE/OPEN FORECAST, and then place the start year and number of end years (including 2008). (Figures 39 - 40) Figure 36

Perform the filtering with T (average time due to the availability of 22 series, one for each day) and then proceed to download data.

Note: for these variables it is not necessary to change the resolution, or use the TRANSFORMATION spreadsheet. (Figure 37)

Figure 39

Figure 37 Figure 40

122 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II

Display the forecasts through the menu: FILE/FORECAST/ In the CPT, the forecasting process for the year of interest SERIES (Figures 41-42) is the same as is performed with individual predictors, by placing the X EOF option in MATRIZ VARIANZA–COVARI- ANZA (MATRIX VARIANCE – COVARIANCE) to preserve the relative importance of the EOF (Figures 45-46-47).

Figure 41

Figure 45

Figure 42 And you obtain the file under the following format: (Figure 43) Figure 46

Figure 43 Continue with the same procedure for second or more vari- Figure 47 ables (or the second area as the case may be) and then group into a single file, which will act as a predictor for the variable under discussion (Figure 44). 2.4 DECISION CRITERIA FOR MANAGING THE CPT RESULTS

1. One of the first indicators to be displayed is the GOOD- NESS INDEX which is the result of the first interaction between the predictors and predicting variables; this is the first condition to follow. If we obtain a negative value, it indicates no correlation or linearity between the information from both variables; for this we must find a better area. Pref- erably, this value should be positive and higher (tendency to have a value of 1). (Figure 48)

Figure 44

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 123 C H A P T E R II

And then modify the years. (Figure 50)

First run of the CPT program, where the index is located. Figure 50

4. Check the canonical correlation coefficient, which is the degree of relationship between the predictor and predic- tant variables (jointly). (Figure 51)

Figure 48. Display in GOODNESS INDEX

2. One of the most important criteria to be considered in the forecasts is the definition of the work period to be used, which is defined by two things:

• The “LENGTH OF TRAINING” PERIOD and, • The “FIRST YEAR OF X TRAINING” PERIOD.

3. In the definition of the climatological period to work on, usually the program defines by default the start and end years of the historical series (in many cases exceeds 30 years). When considering different periods, there will be different results. Figure 51 The climatologic reference period considered was 1971- 2000; many researchers considered the normal since the 5. Only if the previous step is satisfactory, proceed to evalu- start of the historical series until the year preceding the ate the statistical indicators through individual assessment forecast. by station, considering the following route:

The change can be accomplished through the following TOOL/VALIDATION/CROSS VALIDATED/ steps: Enter the CUSTOMIZE menu (configuration), then PERFORMANCE MEASURES / “Climatological Period”. (Figure 49) The analysis is done station by station; at this stage you can not see the stations that exceed the permissible limit of missing data (% MISSING VALUES) (Figure 52)

First, display the chart and compare the red line (observed values) and the green lines (forecast values); highlight if the curves follow the same characteristic pattern, i.e. if a curve rises, the other has to rise and vice versa.

The second display is done in the ROC graph (Relative Op- erating Characteristic) where you can see the curves that are found above the diagonal. If the curve is red, it refers to Figure 49 the predictions made by the model to the “below normal”

124 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II

7. The other values refer to categorical measurements, i.e. the level of accuracy of the model with historical data.

Hit Score: The percentage of hits of the model relative to total predictions made from historical series.

Good forecast The optimum is to have a value close to 100% which would First Step indicate a perfect model.

Hit Skill Score: Is the indicator for evaluating the skill of the model, the percentage of times the result corresponds Bad area to a coincidence. The optimum is to have a value close to ±100% which would indicate a perfect model.

LEPS Score (Linear Error in Probability Space), That calculates a defined result using a table that shows different Figure 52 results of accuracy, depending on the category observed and the previous probabilities of the categories. The prob- category; if it is blue, it refers to the predictions made by ability distribution is transformed to a cumulative probabil- the model in the “above normal” category. It is appropriate ity function. (Figure 54) that the two curves be above the diagonal and approach- ing the upper left corner.

6. Second step, although the statistical indicators are a technical reference, you should fully understand their meanings. The first coefficient of Pearson’s1 and the one of Spearman2 indicate the degree of association that the observed values have with forecast values, and should be approximately 1; the higher these values are, the more favorable the results will be (not good to get values close to -1). (Figure 53)

Figure 54

Gerrity score: Calculates a definite result by using a result table alternative to that used for LEPS results. (Figure 55)

Figure 53

The mean squared error and root mean squared error have the same meaning: they represent the sum of deviations between observed and forecast values, i.e. the error that exists so that predicted values to try to reach the observed value. In a practical way, if the observed and predicted values are similar or nearly identical, means that the error will be zero or nearly zero, hence also its square root.

It should be considered that this indicator is very relative: it is not the same to find a difference between both values (observed and predicted) in a rainy area than in a dry area, for example:

Forecast Observed Error Observations Precipitation Precipitation 430 mm / month 380 mm / month 50 mm Wet zone 10 mm / month 0.0 mm / month 10 mm Dry Zone

Forecast Observed Error Observations Figure 55 Precipitation Precipitation 1. Randall E et al. A beginner’s guide to structural equation mode- 430 mm / month 380 mm / month 50 mm Wet zone ling pg. 38. 2. William H. Press. Numerical recipes: the art of scientific compu- 10 mm / month 0.0 mm / month 10 mm Dry Zone ting pg. 349.

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ROC area (below-normal): Represents the value of the area below the red curve. Defines the area below the ROC curve for forecasts of the below normal category; shows the proportion of times that below normal conditions can be successfully distinguished over other categories. A maximum value and optimal in the model should be 1 (meaning Scheme N° 02. - Process Assessment and De- 100%). cision Making of the results obtained by the CPT ROC area (above-normal): Represents the value of the area under the blue curve. Defines the area below the ROC curve for forecasts of the above normal category and shows the proportion of times above normal conditions can be successfully distinguished over other categories. A maximum value and optimal in the model should be 1 (meaning Run of CPT 100%).

no Goodness Scheme N°01.- Previous processes for the run index -1 of the Climate Predictability Tool (CPT) yes

Joint no Entry formats of the Evaluation CPT: Quarterly, Bi- CCA-1/ monthly, Monthly high?

yes Determination of no new variable and/or Evaluation of predictor area models for each station

Is the NO Initiate alternative predictor process of variable updated? update Graphic evaluation. no Observations vs Similar forecasts? YES

Ready data yes Start year, missing data. If the variable = PP-Y bound=0 Number of years = total of the serie no Configuration of the CTP Graphic evaluation. ROC: above the diagonal?

Number of modes: yes X=10 Y=10 CCA=10 Evaluation of no Good Categorical measurements

yes

Use for individual forecast

Nota: The symbol means tendency or approach

126 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II

If the requirements of Schedules 1 and 2 are met, we are able to use the model to forecast the year preceding each station (individually) that met all these requirements. For this, we carry it out through the menu: (Figure 56)

Figure 56

Place the year to be forecast: 2008 (first year of data in file). (Figure 57) Figure 59

probabilistic values of an above normal condition (superior). (Figure 60)

ABOVE

Figure 57 50% Limit BELOW In the menu: TOOL/FORECAST/MAPS/PROBABILITIES.

Figure 60

If you have values below 50% in category B (below normal) and A (above normal), they are considered normal, as for example, a probability of 25 - 30 - 45, for the CPT to be considered very close to the upper limit but within the “Nor- mal” category. It is noteworthy that many researchers find no significant differences between the values of 25-30-45, considered as either 03 possible cases.

2.6 FREQUENTLY ASKED QUESTIONS RE- LATED TO CPT HANDLING Figura 58 1. What do I do if one of the requirements of Schedule In the probabilistic outcomes only the seasons that met 1 and 2 is not met? all as indicated in Figures 1 and 2 should be considered. The rest of the values will not be considered for making the In that case you should discard the values of the station; forecast table and will be determined with other indexes. therefore, it is not considered in the final results. (Figure 59) 2. How do I consider in the event that the CCC is favor- 2.5 CONSIDERATIONS FOR THE INTERPRE- able and in the individual analysis by stations only a few TATION OF TERCILES are favorable?

The CPT considers among its results by categories values In that case, only those that are both favorable to the ca- above 50% as extreme (superior and inferior). The value of nonical correlation coefficient (CCC) and individual station normal condition is the same as saying the likelihood of statistical indicators will be considered in the eventual out- the climatology. For example, the following graph shows come.

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3. How do I consider if the Pearson and Spearman coef- ficients are high but negative?

They are not considered in the analysis. The values by station are discarded and not considered in the final grouping of forecasts.

4. How do I obtain the limits of the climatology values?

There are two ways to get the climatology:

The first comes from the same original data (data format Example: of the CPT entry corresponding to the variable you want to Year of the forecast:2008 Value of the forecast: 4.527ºC predict= Y); you should add to each column the percentile Confidence Interval: Low 2.980ºC High 6.075ºC values 33 and 66, which corresponds to the limits of the terciles. This value is variable depending on whether the limits of the probabilities are changed. Figure 62

The second way is provided by the CPT program, with the This means that the interval between 12.5 and 30.2 has a command TOOL/FORECAST/SERIES/ top part climate 0.95 probability of containing m. We can also say that if the where the period assumed in the calculation is shown (Fig- procedure for calculating the confidence interval of 95% is ure 61). used many times, 95% of the time the interval will contain the parameter.

Interpretation 2 It is called confidence interval in Statistics to an interval of values around a sample parameter where, with a determined probability or confidence level, the population pa- Period of the climatology rameter to be estimated will be situated. If α is the random Low climatology level, in values. error that you want to assign, the probability will be 1 - α. High climatology level, in values. At a lower level of confidence, the interval will be more precise, but it will commit a greater error. Low climatology level, in probabilities. High climatology level, in probabili- To understand the following formulas, it is necessary to un- ties. derstand the concepts of parameter variability, error, confidence level, critical value and α value.

A confidence interval is thus an expression of the type θ[ 1,

Figure 61 θ2] or θ1 ≤ θ ≤ θ2, where θ is the parameter to be estimated. This interval contains the estimated parameter with a given 5. Does the CPT provide deterministic values in its fore- certainty or confidence level 1-α. casts? Upon providing a confidence interval, it is assumed that The CPT has the advantage of performing multiple opera- population data are distributed in a certain way. It is cus- tions; therefore, it provides multiple results: one is estimat- tomary to do so by normal distribution. The construction ing the values of quantitative forecasts under a given confi- of confidence intervals is performed using the Chebyshev dence level (by default the program calculates with a 68.3% inequality. (Figure 63) confidence level).

This can be displayed after enabling the forecasting by series, following the completion of the run: TOOL/FORE- CAST/SERIES / (Figure 62)

6. What is meant by confidence intervals?

Interpretation 1 A confidence interval is a range of values that has a given probability of containing the parameter being estimated. The 95% and 99% confidence intervals, which have a 0.95 and 0.99 probability of containing the parameter, respec- tively, are the most used.

If the parameter being estimated were m, The confidence interval of 95% will be: Figure 63 12.5 ≤ m ≤ 30.2

128 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R II

This point is the number such that: 9. When do we consider a forecast with uncertain results? P[x ≥ X ] – P[z ≥ X ] – α/2 Uncertainty is an expression of lack of knowledge of a fu- α/2 α/2 ture condition. And in the standardized version: It can result from an absence of information or even because Z- Z there is disagreement on what is known or what could be α/2= - α/2 known. It can have multiple types of sources, from quantifi- Así: able errors in the data to ambiguously defined terminology x – μ or uncertain projections of interpretation. Uncertainty can therefore be represented by quantitative measures (i.e., a P –Z ≤ σ = 1 – α [ α/2 ] range of values calculated by various models) or by quali- √ n tative statements (i.e. reflecting the opinion of a group of experts). Within the CPT all results that have an obtained Performing operations it is possible to clear μ to get the value correspond to the forecaster’s discretion. range: σ σ 10. How does the CPT consider a probabilistic outcome P x–Z ≤ μ ≤ x + Z = 1 – α of 30%-40%-30%? [ α/2 √ n α/2 √ n ] As explained in the previous question, these values are Result is the confidence interval: considered as uncertainties, i.e., any of the categories or σ σ conditions can occur under these conditions. ( x–Z ) α/2 , x + Z √ n α/2 √ n 11. What is the cause of obtaining results with uncertainty? If σ is not known and n is large (i.e. ≥ 30):

s s It could be many causes, including: ( x–Z , x + Z ) α/2 √ n α/2 √ n Bad decision-making in the predictors used; which physically explains the variability on the prediction (value to be s is the standard deviation of a sample. predicted). Approximations for the value Z for standard confidence α/2 The CPT is based on the premise of the existence of a lin- levels are 1.96 for 1 − α = 95% and 2,576 for 1 − α = 99%. ear relationship between predictors and predicting, which doesn’t always exist, which can be a cause of uncertainty. 7. Where can I change the confidence level of my forecasts? The predictors are not defined because they are in a phase of changing astronomical station. Once the CPT is running, proceed to the following route: (Figure 64) The poor quality of the information. In many cases the information from weather stations has fractures in the historical CUSTOMIZE/FORECAST SETTING/ series because of significant changes in their location. Sta- tistically speaking this means that we have practically two different series that have been grouped for the run process with the CPT. (Figure 65)

Figure 64

8. How does the CPT consider probabilistic outcome 50% -10% -40% or 50% -0% -50%? Red Line: 10 followed years off

It is an ambiguity in which any of the scenarios is both pos- Figure 65 sible and not feasible, so it will only be regarded as uncertain. The CPT considers it with the average or normal value The data series have many gaps; missing data also plays an (normal category), but physically is not acceptable. important role in generating forecasts. The CPT program replaces the missing data values by mean values, medians, nearest station and at random.

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 129 C H A P T E R II

The modes are not adequate. Each mode carries a part of 13. How to perform simultaneous testing with two or the variance to be explained from primary data (self values). more predictors? Sometimes the number of adequate modes is not sufficient (usually within the top 5 modes is the explanation of a large The CPT is designed to take only one field of predictors percentage of the total variance). However, sometimes it at a time, but it is possible to obtain software in order to is necessary to increase the number of modes to a recom- produce results with multiple fields. Run the software using mended number of 10 (optional), with which the results are one of the fields of predictors, and with the number of X improved. EOF modes at maximum (this will be the minimum number of grid points and the length of test period). Then proceed Rainfall in the countries near the equator is influenced by to record the scores of principal components using Data several simultaneous changes affecting precipitation and Output. Repeat procedure for other predictor fields. temperature variables. This requires working simultane- ously with multiple predictors (or different areas with only Then we proceed to combine multiple output files of the a single predictor). principal components scores so that the main components for all the predictor fields are in a file. CPT then can be run 12. How do we consider two opposite results obtained with this new file, as the variables read as a non-reference from two different predictive variables? data set. Place in the X EOF the option “covariance matrix” to maintain the relative importance of EOFS. Although it First verify if both have high CCC, and if they statistically will not be possible to see the maps for the merged fields, acceptable; if both are correct, it is advisable to make an all validation results and forecasts will be as if the software assembly with the predictors jointly, which we will have a would have been controlled with multiple input fields. result containing the two involved parameters in the variable to be predicted. Otherwise take the information from Some of the seasonal forecasts in the countries are shown the higher CCC value. in Figure 66.

Figure 66

130 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES CHAPTER III Implementation of numerical models for climate prediction Angel Muñoz CHAPTER III [email protected]

3.1 STEP BY STEP PROCEDURES FOR INSTAL- LATION AND IMPLEMENTATION OF MM5 AND WRF MODELS IN CLIMATE MODE

3.1.1 Operating System

The installation process (with images step-by-step) and the implementation of Scientific Linux, Rocks Cluster and Con- figuration and installation of a computer node is available at:

Scientific Linux: http://mediawiki.cmc.org.ve/index.php/ imagen:Scilinux00.png

Rocks cluster and computer nod: http://mediawiki.cmc. org.ve/index.php/%E2%97%A6_Rocks_Cluster Fig. 68 WRF2 System

3.1.2 Atmospheric Models common to request a library libstdc + +. It is necessary to download (for example from pbone.net) and install it with The atmospheric models considered in the Project are the a simple rpm. fifth generation of the Mesoscale Model (MM5) and the Weather and Research Forecast model (WRF). The follow- 2. Download and install NCAR ing pages show their installation and configuration. The same models, with appropriate modifications, are set as www.ucar.edu climate versions. These versions have been called CMM5 and CWRF. Installation is simple. It is required to follow the Setup in- structions. The MM5 model is divided into multiple modules and sub- programs. Figure No. 66 presents a schematic diagram of Note: It is suggested that you install it in: /usr/local/ncarg. MM5. The same way, in Figure No. 67 presents a diagram of the WRF model. 3. Download MM5

The packages needed are: TERRAIN, REGRID, LITTLE_R, INTERPF, MM5. ftp://ftp.ucar.edu/mesouser/MM5V3

4. Edit the /etc/bashrc

The last lines should say:

export PATH=$PATH:/opt/intel/fc/9.1.036/bin:/usr/ local/ncarg/bin exportLD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/intel/ fc/9.1.036/lib:/usr/local/ncarg/lib export NCARG_RO OT=/usr/local/ncarg Fig. 67 System Model MM51 The situation showed above corresponds to an example. 3.1.2.1 MM5 It is necessary to set the paths to the correct directories of the compiler. To load the newly introduced environment 1. Download and install Intel Fortran variables, it is enough to write: source/etc/bashrc. www.intel.com 5. To verify that the process is correct, consider the fol- Note: There is a free non-commercial license. It is relatively lowing steps:

1. University Corporation for Atmospheric Research, Weather Re- 5.1. IFC: write ifort-v (It should display the installed ver- search and Forecasting Model users`s guide. Chapter 1 sion). http://www.mmm.ucar.edu//wrf/users/docs/user_guide_V3.1/ 5.2. NCAR: idt (It should open a graphical window) users_guide_chap1.htm 2. University Corporation for Atmospheric Research http://www.mmm.ucar.edu//wrf/users/docs/user_guide_V3.1/ 6. Create a directory (e.g. /datos/MM5) and decom- users_guide_chap1.htm#WRF_Modeling_System press TERRAIN:

132 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III

> cd /datos > mkdir MM5 9. Now we proceed to compile: > tar -xvzf TERRAIN.TAR.gz (clearly this file MUST be in this directory) > make intel > make terrain.deck 7. Verify if the libg2c library is installed 10. Download the necessary data for TERRAIN as follows and decompress it If the libg2c library is not installed, proceed to install it now. If it has a different name, create the symbolic link. > cd /datos/MM5/DATOS Note: This library can be downloaded online, it is also avail- > wget ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_ able in the gfortran. For instance, in [email protected]: DATA/* > ls-1 Another way: you can download it from: http://www.cmc. > gunzip *.gz org.ve/descargas/libg2c.so > tar-xvf archivo.TAR 10.1. Modify terrain.deck.intel [root@Aquila TERRAIN]# find /usr ¬name “*libg2c*” This search the library > vi terrain.deck.intel /usr/local/matlab/sys/os/glnx86/libg2c.so.0 /usr/local/matlab/sys/os/glnx86/libg2c.so.0.0.0 And modify: /usr/lib/libg2c.so.0 /usr/lib/gcc/i386¬redhat¬linux/3.4.3/libg2c.so > set ftpdata = false /usr/lib/gcc/i386¬redhat¬linux/3.4.3/libg2c.a > Set the following for ftp ’ in g30 sec /usr/lib/libg2c.a > elevation data from USGS ftp site /usr/lib/libg2c.so.0.0.0 > set Where30sTer = /mnt/data/terrain_data [root@Aquila TERRAIN]# ln -¬s /usr/lib/gcc/ i386¬redhat¬linux/3.4.3/libg2c.so /usr/lib/libg2c. The result should be as follows: so

Place it in /usr/lib and perform an additional symbolic link #set ftpdata =true as follows: set ftpdata = false #set Where30sTer = ftp set Where30sTer = /datos/MM5data/DATOS > ln -s /usr/lib/libg2c.so /usr/lib/libg2c.so.0 Then proceed to link: 8. Edit the TERRAIN Makefile

Find the line that corresponds to the intel compiler and > ln -s /datos/MM5data/DATOS/* TERRAIN/Data/ modify the PATH to lg2c: 11. Compile TERRAIN again and run

> vi Makefile > make terrain.deck > /intel This finds the appearance of the word after the > ./terrain.deck.intel slash. Note: This compiles the code again. When finished, log The paragraph should be as follows: into terrain.print.out and make sure the two last lines show: intel: > tail 2 terrain.print.out echo “Compiling for Linux using INTEL compiler” If the process is correct, at the end of the run this should ( $(CD) src ; $(MAKE) all \ appear: “RM = $(RM)” “RM_LIST = $(RM_LIST)” \ == NORMAL TERMINATION OF TERRAIN PROGRAM == “LN = $(LN)” “MACH = 99999 SGI” \ “MAKE = $(MAKE)” “CPP = / Then write lib/cpp” \ “CPPFLAGS = -I. C traditional D$(NCARGRAPHICS) idt TER.PLT “ \ “FC = ifort “ “FCFLAGS = 12. Create a download folder for “TERRAIN DATA” -I. -w90-w95-convert big_endian “\ “LDOPTIONS = -i_dynamic” “CFLAGS Download from there as needed. = -I. “\ “LOCAL_LIBRARIES=-L$(NCARG_ROOT)/lib -L/usr/X11R6/ $cd $LOQUESEA/mm5 lib -lncarg -lncarg_gks-lncarg_c-lX11-L/usr/lib $mkdir DATOSls -lg2c” ) ; \ $ cd DATOS ( $(RM) terrain.exe ; $(LN) src/terrain.exe.) ; $ wget ftp://ftp.ucar.edu/mesouser/MM5V3/TERRAIN_ DATA/*

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 133 C H A P T E R III

$ for x in ‘ls 1 *.gz‘; do gunzip $x; done $ make plotfmt

13. Unzip REGRID If there are no errors: $./plotfmt ../ON84:1993¬03¬14_00 In /datos/DatAquila/Meteo/mm5 and compile $ idt gmeta

$ make intel Go to the regridder directory and:

Then download the data: NCEP_ON84.9303 in /datos/ $ ./regridder Meteo/DatAquila/mm5/DATOS, which is an input file for pregrid. 17. If everything is correct, in the last step the following file will be created: REGRID_DOMAIN1 wget c –passiveftp ftp://ftp.ucar.edu/mesouser/ MM5V3/TESTDATA/NCEP_ON84.9303 18. For LITTLE_R first proceed to decompress it

14. Log into pregrid folder $ tar xvzf LITTLE_R.TAR.gz

Edit pregrid.csh the lines that follow (The file created should be placed in /datos/DatAquila/ Meteo/mm5) set DataDir =/datos/DatAquila/Meteo/mm5/DATOS 19. Log into the Makefile of LITTLE_R (in the intel op- 15. Run pregrid.csh tions)

$ ./pregrid.csh -L/usr/lib/gcclib/i386redhatlinux/3.3.2

It should read: To ********** -L/usr/lib lg2c. Normal termination of program PREGRID_ON84 ********** It should read as follows: mv SNOW:19930313_00 ../ON84_SNOW:19930313_00 mv SNOW:19930313_12 ../ON84_SNOW:19930313_12 “LOCAL_LIBRARIES= -L$(NCARG_ROOT)/lib -L/usr/ mv SNOW:19930314_00 ../ON84_SNOW:19930314_00 X11R6/lib -lncarg -lncarg_gks -lncarg_c -lX11 -L/usr/lib - lg2c” >> macros_ Now little_r ; \ cd /datos/DatAquila/Meteo/mm5/REGRID/pregrid/ ( $(CD) src ; $(MAKE) $(PROGS) ) on84/.. Note: Where the above was now reads -L/usr/lib If the process is right, this should appear in the pregrid di- -lg2c. rectory (the result of ls-l): 20. Download test data for LITTLE_R. Doc/ nise/ ON84_SNOW:19930313_00 pregrid.csh* era/ nnrp/ ON84_SNOW:19930313_12 pregrid_era40_int. wget c –passiveftp ftp://ftp.ucar.edu/mesouser/ csh* MM5V3/TESTDATA/input2little_r.tar grib.misc/ on84/ ON84_SNOW:19930314_00 pregrid. namelist Proceed to place it in: /datos/DatAquila/Meteo/mm5/ Makefile* ON84:19930313_00 ON84_SST:19930313_00 RE- DATOS then decompress the files as follows: ADME_ERA40 navysst/ ON84:19930313_12 ON84_SST:19930313_12 $ tar xvf input2little_r.tar toga/ ncep.grib/ ON84:19930314_00 ON84_SST:19930314_00 and the following files should be obtained: (ls-l) util/ Test_data 16. Find in the pregrid directory the useful directory; Test_data/REGRID_DOMAIN1.gz there should be a file called plotfmt. Test_data/surface_obs_r:19930313_21.gz Test_data/obs13_00.gz To compile the file, you should make the following changes Test_data/obs14_00.gz to the Makefile: Test_data/obs13_06.gz Test_data/surface_obs_r:19930313_18.gz NCARG_LIBS= ?L$ (NCARG_ROOT) /lib \ Test_data/surface_obs_r:19930313_15.gz ?lncarg ?lncarg_gks ?lncarg_c \ Test_data/surface_obs_r:19930313_12.gz ?L/usr/X11R6/lib ?lX11 ?lm \ Test_data/obs13_18.gz ?L/opt/intel/fc/9.1.036/lib ?L/usr/lib ?lg2c Test_data/obs13_12.gz Test_data/surface_obs_r:19930313_09.gz Then Test_data/surface_obs_r:19930313_06.gz Test_data/surface_obs_r:19930313_00.gz

134 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III

Test_data/surface_obs_r:19930314_00.gz Test_data/surface_obs_r:19930313_03.gz Note: for each data it is necessary to modify namelist. input. Place the data that you created in the folder TEST_ and write: 23. OPTIONAL: Install RAWINS

$ gunzip *.gz The installation will not be explained in this guide.

It will get the following files: 24. Install INTERPF obs13_00 INTERPF is responsible for doing pressure interpolations. obs14_00 obs13_12 Go to the MM5 directory and write (in this case the tar.gz is obs13_06 in the directory immediately above). obs13_18 REGRID_DOMAIN1 $ tar xvzf ../INTERPF.TAR.gz surface_obs_r:19930313_06 surface_obs_r:19930313_18 25. Now simply surface_obs_r:19930313_09 surface_obs_r:19930313_21 $ cd INTERPF surface_obs_r:19930313_00 $ make intel surface_obs_r:19930313_12 $ ./interpf surface_obs_r:19930314_00 surface_obs_r:19930313_03 The above should create the files: surface_obs_r:19930313_15 MMINPUT_DOMAIN1, LOWBDY_DOMAIN1 y BDYOUT_DOMAIN1 That will be used by MM5. All these files will be located in: /datos/DatAquila/Meteo/ mm5/DATOS/Test_data 26. MM5:

21. Modify namelist.input It should begin by decontaining and decompressing: Go to the MM5 directory and The result should be: $ tar xvzf ../MM5.TAR.gz &record2 fg_filename = ‘../REGRID/regridder/REGRID_ DOMAIN1’ obs_filename= ‘/datos/DatAquila/Meteo/mm5/ Now go to the Run directory (which is within the MM5) and DATOS/Test_data/obs13_00’ perform the following symbolic links: ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/ obs13_12’ $ ln s ../../INTERPF/MMINPUT_DOMAIN1 . ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/ $ ln s ../../INTERPF/BDYOUT_DOMAIN1 . obs14_00’ $ ln s ../../INTERPF/LOWBDY_DOMAIN1 . sfc_obs_filename= ‘/datos/DatAquila/Meteo/mm5/DA- $ ln s ../../TERRAIN/TERRAIN_DOMAIN2 . TOS/Test_data/surface_obs_r:19930313_00’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- 27. Go back to the MM5 directory face_obs_r:19930313_03’ ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- And edit the section corresponding to 3I2 (INTEL with Intel face_obs_r:19930313_06’ Fortran Compiler) of configure.user. ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- face_obs_r:19930313_09’ The result should be shown as follows: ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- face_obs_r:19930313_12’ # ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- # 3i2. PC_INTEL (LINUX/INTEL) face_obs_r:19930313_15’ # ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- RUNTIME_SYSTEM = “linux” face_obs_r:19930313_18’ FC = ifort ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- FCFLAGS = I$(LIBINCLUDE) O2 tp p6 pc 32 convert face_obs_r:19930313_21’ big_endian ‘/datos/DatAquila/Meteo/mm5/DATOS/Test_data/sur- CPP = /lib/cpp face_obs_r:19930314_00’ / CFLAGS = O CPPFLAGS = I$(LIBINCLUDE) 22. Run the test: LDOPTIONS = O2 tp p6 pc 32 convert big_endian LOCAL_LIBRARIES = $ ./little_r MAKE = make i R After a few minutes a pair of files will be created. In particular, LITTLE_R_DOMAIN1 is necessary to run MM5.

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 135 C H A P T E R III

28. Compile and run: OROSHAW = 0, ;include effect of orography shadowing; ONLY has an effect if LEVSLP is also set; 0=no effect (de- $ make fault); 1=orography shadowing taken into account - NOT $ make mm5.deck AVAILABLE FOR MPI RUNS. $ ./mm5.deck IMOIAV = 1, 1, Schematic of variable humidity. Depend- If the process is successful this should be displayed: ing on the case, for weather, select 1 or 2; 0 - not used; 1 - is used without additional data; 2 - used with data from Make [1]: Leaving directory `/datos/DatAquila/Me- additional moisture. teo/mm5/MM5/Run’ This version of mm5.deck stops after creating namel- OROSHAW controls whether or not to include shadow ef- ist file mmlif. fects due to orography in executions. Obviously, it is more Please run code manually. physical, and costs more. If you wish to activate it , set vie mar 30 17:39:26 VET 2007 LEVSLP, indicating the nest (1 = father, 2 = child, 3 = grand- son, etc.) from which OROSHAW begins to be used. Now: 4.- Initial conditions: $ cd Run $ ./mm5.exe An important aspect corresponds to the way of the analysis data is assimilated for the initial conditions. This is per- 1.- Settings: formed as follows:

In configure.user (/datos/CMM5/MM5/configure. IBOUDY = 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, ;boundary user) all the information about the settings can be found conditions (Section 6 of the file). In Section 5 of that file the parameters ; (fixed, time-dependent, relaxation -0,2,3) have to be considered carefully: IIf the domain is too large (all of Brazil, all of South America, MAXNES = N (Here, the maximum number of domains to etc ...) you should use a relaxed scheme of the boundary run in mm5.exe should be set). conditions (for references see the Online MM5 Manual or refer to Davies & Turner, Quart. J. Roy. Meteor. Soc, 103, MIX,MJX is the pre-dimensioning that is done for the ar- 225-245 (1977)). For the remainder ones the time-depen- rays along the north-south and east-west axes. If a domain dent scheme can be used. has been created in which the dimensions north-south or east-west exceed these parameters, you must increase MIX 5.- The TSM item variable throughout the execution is and MJX. also important.

IMPORTANT: each time you change the configure.user It should be turned it in on in the following option: should type: make clean; make (for the changes to take ISSTVAR= 1, effect). 6.- This might be useful: 2.- On the other hand there is the mm5.deck (/datos/ CMM5/MM5/mm5.deck). IFSNOW = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ;SNOW COVER EFFECTS - 0, 1, 2 Most important aspects to consider: ; ;0 - no effect, 1 - with effect, 2 - simple snow model TIMAX = NNN (Total number of minutes that the forecast will last: NNN minutes to the future). 7.- Now proceed to seek this section:

TISTEP = (is the delta T, in seconds the temporal integra- NEST AND MOVING NEST OPTIONS tion step. If CFL violations occur, this step should be decreased, and is linked to the spatial resolution chosen. LEVIDN = 0,1,2,1,1,1,1,1,1,1, “NEST- The recommendation is to use a little less than 3 times the ED” LEVEL distance between the points assumed in TERRAIN for the NUMNC = 1,1,1,3,1,1,1,1,1,1, IDENT. thicker domain-the one with lower resolution). MOTHER DOMAIN NESTIX = 39, 13, 19, 46, 46, 46, 46, 46, 46, 3.- Other important options: 46,NORTH-SOUTH SIZE NESTJX = 45, 22, 13, 61, 61, 61, 61, 61, 61, RADFRQ = 30. (Indicates how often atmospheric radiation 61,EAST-WEST SIZE subroutines are calculated in minutes. This value is appro- NESTI = 1, 20, 18, 1, 1, 1, 1, 1, 1, priate in order to start). 1, ORIGIN IN NORTH-SOUTH NESTJ = 1, 13, 9, 1, 1, 1, 1, 1, 1, LEVSLP = 9, ;nest level (correspond to LEVIDN) at which 1, ORIGIN IN EAST-WEST solar radiation needs to be taken into account for orogra- XSTNES = 0., 0.,900., 0., 0., 0., 0., 0., phy; set the large to switch off; only have an effect for very 0., 0., MINUTE THIS DOMAIN IS INITIALIZED high resolution model domains. XENNES =259920.,259920.,1440.,720.,720.,720.,720.,7 20.,720., MINUTE THE CORRESPONDING EXECUTION ENDS.

136 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III

It is necessary to proceed to adjust each requirement that Define the time interval to process. is requested, in accordance with the provisions in terrain. INTERVAL = 21600 # Time interval (seconds) to pro- namelist cess. # This is most sanely the same as the time interval And just below, put the options as follows: for # which the analyses were archived, but you can re- IOVERW = 1, 1, 1, 1, 1, 1, 0, 0, 0, ally 0, ; overwrite nest input # set this to just about anything, and pregrid will ; 0=interpolate from coarse mesh (for nest domains); The (INTERVAL) step usually takes 6 hours. You can check ; 1=read in domain initial conditions the directory listing directly. ; 2=read in nest terrain file Finally: 3.1.2.2 CMM5 set VT3D = ( grib.misc/Vtable.NNRP3D ) 1.- TERRAIN set VTSST = ( grib.misc/Vtable.NNRPSST ) set VTSNOW = ( grib.misc/Vtable.xxxxSNOW ) Check that terrain. namelist registers as “NSTTYP” 1 set VTSOIL = ( grib.misc/Vtable.xxxxSOIL ) for the first domain and 2 for others who are using it. This ensures two-way feedback in the mesh. 3.- REGRIDDER (inside REGRID): /datos/CMM5/RE- GRID/regridder/namelist.input 2.- PREGRID (inside REGRID): /datos/CMM5/REGRID/ pregrid/pregrid.csh As previously discussed, if you carried out the exercises described, you should proceed to set dates in a basic way. The first is “decontain” (spread) the files to work on. For And the ptop_in_Pa, which must match with the first_guess. example, If the installation process was followed correctly, the program should work without changes. tar -xvf archivo.pgb.f00.tar tar -xvf archivo.grb2d.tar REMEMBER: Regridder is run once per domain tar -xvf A##### tar -xvf A##### 4.- INTERPF (In /datos/CMM5/INTERPF/namelist.input):

After this step, the following changes occur (this illustrates The first two lines must show the following: just one example, it should be adjusted according to the needs of the users): &record0 input_file= ‘../REGRID/regridder/REGRID_DOMAIN1’ / set DataDir = /datos/2005/1ero Here, later you vary the domains, an interpf run for each. Here is PATH where the data is. The following section may vary for some cases. set InFiles = ( ${DataDir}/pgb.f00####*) &record3 Instead of ### place the beginning of the numbers of the p0 = 1.e5 ! base state sea-level pres (Pa) year in question. Ex: pgb.f000506*. tlp = 50. ! base state lapse rate d(T)/d(ln P) ts0 = 275. ! base state sea-level temp (K) set SRC3D = GRIB # Many GRIB-format datasets tiso = 0./ ! base state isothermal strato- set SRCSST = $SRC3D spheric temp (K) set InSST = (${DataDir}/grb2d0506*) This corresponds to the definition of the base state from which MM5 defines number of other variables/parameters. As before. Indicate the beginning of the files to be used. Detailed explanation with the equations can be found at: The * takes all related. www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_ notes/INTERPF/interpf.htm In this section, adjust the dates: 3.1.2.3 WRF START_YEAR = 2005 # Year (Four digits) START_MONTH = 06 # Month ( 01 - 12 ) 1.- Downloads: START_DAY = 01 # Day ( 01 - 31 ) START_HOUR = 06 # Hour ( 00 - 23 ) www.mmm.ucar.edu/wrf/src/WRFV2.2.1.TAR.gz (WRF as such). Note: It should begin in 06 www.mmm.ucar.edu/wrf/src/WPSV2.2.1.TAR.gz (WPS, the preprocessor). END_YEAR = 2005 # Year (Four digits) END_MONTH = 06 # Month ( 01 - 12 ) Topography data: END_DAY = 30 # Day ( 01 - 31 ) www.mmm.ucar.edu/wrf/src/wps_files/geog.tar.gz END_HOUR = 18 # Hour ( 00 - 23 )

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 137 C H A P T E R III

Additional necessary libraries: /usr/local/lib/libnetcdf.a www.mmm.ucar.edu/wrf/src/wps_files/jasper-1.701.0.tar.gz /usr/local/lib/libnetcdf_c++.la www.mmm.ucar.edu/wrf/src/wps_files/libpng-1.2.12.tar.gz /usr/local/lib/libnetcdf_c++.a www.mmm.ucar.edu/wrf/src/wps_files/zlib-1.2.3.tar.gz /usr/local/lib/libnetcdf.la

2.- These files should be stored, for example, in a folder 5.- Proceed now to /datos and create the CWRF folder and called tars in /. Start decompression. The first things are unzip it: extra in this case: mkdir CWRF ZLIB: cd CWRF ----- tar -xvzf /TARS/WRFV2.2.1.TAR.gz cd /opt tar -xvzf /TARS/WPSV2.2.1.TAR.gz tar -xvzf /TARS/zlib-1.2.3.tar.gz cd zlib-1.2.3 6.- Locate the WRFV2 folder ./configure make cd WRFV2 make install It is needed to add new lines to /etc/bashrc. These are JASPER: described below: ------cd /opt export JASPERLIB=/opt/jasper-1.701.0 tar -xvzf /TARS/jasper-1.701.0.tar.gz export JASPERINC=/opt/jasper-1.701.0 cd jasper-1.701.0 ulimit -s unlimited ./configure make To update the environment variables, proceed as usual: make install source /etc/bashrc LIBPNG: ------From now on, you may follow one of two options. The first cd /opt is to set from zero WRF and the second is to download the tar -xvzf /TARS/libpng-1.2.12.tar.gz configuration file. In the same directory you should write: cd libpng-1.2.12 ./configure ./Configure make make install The following appears: ** WARNING: No path to NETCDF and environment vari- 3.- Now proceed with netcdf. To avoid confusion with the able NETCDF not set. versions, we suggest downloading the version available on ** would you like me to try to fix? [y] the server: www.cmc.org.ve/descargas/netcdf.tar.gz Choose “y” and include the PATH: /usr/local/include 3.1.- Place it in /TARS (or wherever you are placing tar con- /usr/local/lib tainers). Unzip: Every time that it asks. If the process is successful, a menu tar -xvzf netcdf.tar.gz appears (at the beginning it indicates that it recognizes the cd netcdf-3.6.2 paths to the JASPER library): export FC=ifort ./configure Please select from among the following supported make; make install platforms. 1. PC Linux i486 i586 i686, PGI compiler (Single- If the tar does not work with netcdf.tar.gz, write threaded, no nesting) netcdf.tar.Z 2. PC Linux i486 i586 i686, PGI compiler (single threaded, allows nesting using RSL without MPI) 4.- Check that the last step is correct, it is crucial for WRF. 3. PC Linux i486 i586 i686, PGI compiler SM- An ls /usr/local/include/netcdf* debe mostrar: Parallel (OpenMP, no nesting) 4. PC Linux i486 i586 i686, PGI compiler SM-Par- /usr/local/include/netcdfcpp.h allel (OpenMP, allows nesting using RSL without MPI) /usr/local/include/netcdf.inc 5. PC Linux i486 i586 i686, PGI compiler DM- /usr/local/include/netcdf.h Parallel (RSL, MPICH, Allows nesting) /usr/local/include/netcdf.mod 6. PC Linux i486 i586 i686, PGI compiler DM- /usr/local/include/netcdf.hh Parallel (RSL_LITE, MPICH, Allows nesting) 7. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, Perform: ifort compiler (single-threaded, no nesting) ls /usr/local/lib/libnetcdf* 8. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort compiler (single threaded, allows nesting us- It should show: ing RSL without MPI)

138 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III

9. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ress (24 hours total) can be seen on the screen. This ex- ifort compiler (OpenMP) ample performs downscaling time mode with two domains. 10. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, If at the end we can see the message: ifort compiler SM-Parallel (OpenMP, allows nesting using RSL without MPI) COMPLETED SUCCESFULLY 11. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, ifort+icc compiler DM-Parallel (RSL, MPICH, allows Then it means the installation of the WRF is correct. nesting) 12. AMD x86_64 Intel xeon i686 ia32 Xeon Linux, 9.- WPS (WRF PREPROCESING SYSTEM) Proceed to com- ifort+gcc compiler DM-Parallel (RSL, MPICH, allows pile the WPS. nesting) 13. PC Linux i486 i586 i686, g95 compiler (Single- cd /datos/CWRF/WPS threaded, no nesting) 14. PC Linux i486 i586 i686, g95 compiler DM- You will be able to distinguish in these executable some di- Parallel (RSL_LITE, MPICH, Allows nesting) rectories similar to those used in the WRFV2 runs. First run: Enter selection [1-14] : 10 ./configure The selection must be “10”. If you desire to test WRF, select 7 (makes it impossible to create nesting) or 8 (with nests). After selecting the appropriate option, run:

7.- Compiling the WRF. Once the above steps are done, ./compile proceed with the compiling: The compilation is considerably shorter than the WRF. ./compile em_real > log.log 10.- Domain Wizard. There is a multiplatform application WRF is legendary for having a very long compilation. Wait (in Java) that can be used to perform WRF preprocessing. It at least 40 minutes. If you wish verify the state of compila- is a kind of GUI for WPS. It can be downloaded at: tion, perform a vi log.log in the same directory. Some http://wrfportal.org/domainwizard/WRFDomainWizard.zip notifications of compilation can be seen directly in the directory when you send it to do the job. These are important, You should place it either in the WPS or WRF and proceed particularly if there is an error, a screen will appear where we to decompress: ordered the “compile em_real. gunzip WRFDomainWizard.zip 8.- Testing the WRF. If the process is successful, a way to do preliminary test is: Then proceed to run: ls run ./run_DomainWizard

You should see some symlinks: nup.exe, ndown.exe and It is important to remember and know precisely where each especially real.exe y wrf.exe. If they are highlighted in red, file is. We recommend creating, on WRF directory level something in the process has failed. Then you proceed to (which contains the WPS and WRFV2), a directory called perform an additional test: a short run of WRF. Domain (Dominios), where you can place the various domains that are created. To do this, you need to download some test data in WRF intermediate format, available at: Hereafter it will be possible to perform a forecast run in http://www.mmm.ucar.edu/wrf/src/data/jan00_wps.tar.gz mode time with WRF.

Another method is to do it directly into the terminal: 3.1.2.4 CWRF cd test/em_real Conceptually, the climate mode configuration is similar to wget -c http://www.mmm.ucar.edu/wrf/src/data/jan00_ the CMM5. wps.tar.gz tar -xvzf jan00_wps.tar.gz 1.- The first is to tell the WRF to update TSM throughout an execution. This will create even additional files that may be The following is the initialization of WRF for testing. If the read on the way. process is correct, these commands will not generate errors: Go to the WRFV2 directory and edit the namelist.input. The lines to be modified in each record are (if they do not exist, cp namelist.input.jan00 namelist.input you must create them) ./real.exe &time_control When finished, run the WRF itself. auxinput5_inname = “wrflowinp_d”, auxinput5_interval = 180, ./wrf.exe io_form_auxinput5 = 2

The process will take a few minutes. The execution prog- &physics

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 139 C H A P T E R III sst_update = 1, = 99, GFDL (Eta) scheme; adjust co2tf = 1

With this, the real.exe will write a wrflowinp_d## file type for ra_sw_physics (max_dom) each active domain (DO NOT change the ) with option longwave radiation TSM information. The interval is in minutes. = 0, without longwave radiation option = 1, Dudhia scheme If the process is right, after running real.exe you will ob- = 2, Goddard short wave serve: = 3, cam scheme also must set levsiz, paerlev, cam_ abs_dim1/2 (see below) wrfinput_d01 = 99, GFDL (Eta) longwave (semi-supported) wrfbdy_d01 also must use co2tf = 1 for ARW wrflowinp_d01 radt (max_dom) If there is only a single domain, those corresponding to = 30, ; minutes between radiation physics calls other domains will appear. recommend 1 min per km of dx (e.g. 10 for 10 km)

2.- There is available documentation on the parameteriza- nrads (max_dom) tions of the WRF by J. Dudhia3. The changes to the physics = FOR NMM: number of fundamental timesteps between of WRF (and CWRF) set out are shown below: calls to shortwave radiation; the value is set in Registry.NMM but is overridden by namelist value; REMEMBER: different settings can be placed between do- radt will be computed from this. mains, but you should always check the compatibility between them. In some items the same settings are placed for nradl (max_dom) all. This is not necessarily correct, but may be considered = FOR NMM: number of fundamental timesteps between as an option: calls to longwave radiation; the value is set in Registry.NMM but is overridden by namelist value. &physics mp_physics (max_dom) micro physics options co2tf = 0, without micro physics CO2 transmission function flag only for GFDL radia- = 1, Kessler scheme tion = 2, Lin et al. scheme = 0, read CO2 function data from pre-generated file = 3, WSM 3-class simple ice scheme = 1, generate CO2 functions internally in the fore- = 4, WSM 5-class scheme cast = 5, Ferrier (new Eta) micro physics = 6, scheme for graupel WSM 6-class ra_call_offset = 8, Thompson et al. scheme radiation call offset = 98, scheme (to disappear) of simple ice NCEP = 0 (no offset), =-1 (old offset) 3-class = 99, scheme (to disappear) NCEP 5-class cam_abs_freq_s = 21600 CAM clearsky longwave absorption calcula- The following are valid if mp_physics =! 0, to maintain Qv > tion frequency (recommended minimum value to speed = 0, and adjust the other fields of humidity to be less than scheme up) or equal to a determined critical value. levsiz = 59 for CAM radiation input ozone levels mp_zero_out = 0, ; without adjustment of any humidity field paerlev = 1, ; except for Qv, all the other arrangements = 29 for CAM radiation input aerosol levels of humidity will be nulled = 2, ; Qv >=0, Every other arrangements of hu- cam_abs_dim1 midity will be nulled at certain limit. = 4 for CAM absorption save array mp_zero_out_thresh cam_abs_dim2 = 1.e-8 ; Critical value, under the same, all the = e_vert for CAM 2nd absorption save array humidity arrangements, s ; except Qv, will be nulled (kg/kg) f_sfclay_physics (max_dom) surface-layer option (old bl_sfclay_physics option) ra_lw_physics (max_dom) = 0, no surface-layer option longwave radiation = 1, Monin-Obukhov scheme = 0, without longwave radiation = 2, Monin-Obukhov (Janjic) scheme = 1, rrtm scheme = 3, CAM scheme (adjust levsiz, paerlev, cam_abs_ 3.1.3 Oceanographic Models dim1/2 below) 3.1.3.1 ROMS 3. J. Michalakes, J. Dudhia et al. The weather Research and forecast model: Software architecture and performance. 11th ECMWF The Agrif version of Rom is easy to use, and is not very de- workshop on the Use of High Performance Computing in Meteo- rology, Reading U.K., 2004 manding in the amount of data to start the runs. Agrifer

140 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III version has the advantage of being easy to install. It is nec- Mmax = 2; % last forcing month essary to copy the files downloaded from Romstools page % and to copy them to HDD. After running matlab from the Dmin = 1; % Day of initialization /Roms/Romstools/Run folder, almost everything can be Hmin = 0; % Hour of initialization done with a significantly good level of feedback. As soon Min_min = 0; % Minute of initialization as the Rutgerts version is fully understood, some particular Smin = 0; % Second of initialization comparisons will be made. % SPIN_Long = 0; % SPIN-UP duration in Years First you must go to ../Roms/Romstools/Run and modify via terminal using the preferred text editor, the romstools_ To execute the run, it is necessary to conduct the follow- param.m file within it. The first thing to edit is: ing analysis (not all are essential but it is recommended to perform all of them, to ensure greater accuracy in results) ROMS_title = ‘Pacifico’; % It is recommended to place the ROMS file names (grid, forcing, bulk, climatology, initial). name of the area to be studied for better control of the ROMS_config = ‘CMC’; %. runs. A name for the type of Then from Matlab: configuration. (Later there will be other files that will have the same ROMS_title and ROMS_config). >>start >>make_grid Then proceed to place the mesh dimensions of the area to >>make_NCEP be studied by placing the coordinates of the place: >>make_clim >>make_bry % Grid dimensions: % (in the case that make_NCEP it was not possible to do the lonmin = -148; % Minimum longitude [degree east] forcing and the bulk) lonmax = -75; % Maximum longitude [degree east] >>make_forcing latmin = -10; % Minimum latitude [degree north] >>make_bulk latmax = 10; % Maximum latitude [degree north] you return to the terminal where you run the executable The resolution of the grid in degrees: jobcomp

% Grid resolution [degree] % ./jobcomp dl = 1; %maximum is 1, minimum used by the CMC, dl=1/32; and finally Number of vertical levels (must be the same in param.h) % ./roms roms.in N = 32; If the process is correct, from matlab write Then: >>roms_gui % Minimum depth at the shore [m] (depends on the resolution, % rule of thumb: dl=1, hmin=300, dl=1/4, and through the menu the roms_avg.nc file opens in hmin=150, ...) % This affect the filtering since it ROMSFILES works on grad(h)/h. % hmin = 300; % % Maximum depth at the shore [m] (to prevent the generation % of too 3.1.4 Displayers big walls along the coast) % hmax_coast = 500; % Slope parameter (r=grad(h)/h) maximum value for 3.1.4.1 GrADS topography smoothing % 1.- Create a directory in /usr/local/GrADS rtarget = 0.02; %0.025; % GSHSS user defined coastline (see m_map) % XXX_f. 2.- Download mat Full resolution data % XXX_h.mat High resolution data % XXX_i.mat Intermediate resolution data wget ftp://ftp.ucar.edu/mesouser/MM5V3/MM5toGrADS. % XXX_l.mat Low resolution data % XXX_c.mat Crude TAR.gz resolution data % coastfileplot = ‘coastline_l.mat’; 3.- Unzip tar -xvzf MM5toGrADS.TAR.gz coastfilemask = ‘coastline_l_mask.mat’; 3.1.4.2 Vis5D Finally the last section to modify in order to meet the minimum requirements for a run is: TOVIS5D

% 6 Temporal parameters (used for make_tides, make_ 1.- $tar -xvzf tovis5d. $tar.gz NCEP, make_OGCM) This creates the TOVIS5D folder. Yorig = 2008; % reference time for vector time % in roms initial and forcing files % 2.- Edit the Makefile as follows: Ymin = 2008; % first forcing year Ymax = 2008; % last forcing year linux: Mmin = 1; % first forcing month cd src/ ; $(MAKE) target \

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 141 C H A P T E R III

“FC = ifort” \ “FCFLAGS =free DLINUX I. if ( ! e convert big_ endian” \ $1 ) then “CCFLAGS =g DLITTLE DUNDERSCORE c” \ echo “The file $1 does not exist” “LIBS =Vaxlib “ $(RM) tovis5d ; $(LN) src/tovis5d . 3.- Then proceed to the compilation: Then tovis5d.csh must show as follows: $ make linuxx $ make linuxopengl if ( ! e $1 ) then echo “The file $1 does not exist” If the machine has NvidiaTMGraphics Card, place: exit 1 endif $ make linuxnvidia. tovis5d $1 >&! tovis5d.log 4.- Access the /datos/MM5Vis directory to type: 3.- Compile $vis5d vis5d.file $ make linux The models shown are running experimentally in the vari- The options for the prediction: ous countries, some of which are shown in Fig 69.

!/bin/csh f set echo cat >! user.in << EOF &userin view_times=0.,3.,6.,9.,12.,15.,18.,21.,24.,27., gracetime_in_seconds=300., model_version = ‘mm5v3 output’, new_fields = ‘the’, discard_fields = ‘RAD’, ‘PP ‘, interp_2_height = .true., output_terrain = .false. / &end

4.- Go to /datos/MM5Vis to type

$tovis5d MMOUT_DOMAIN1

It should show ======normally ended ======

Vis5D

1.- The program can be downloaded from the following links: ftp://ftp.ssec.wisc.edu/pub/vis5d5.1/vis5d5.1.tar.Z ftp://ftp.ssec.wisc.edu/pub/vis5d/vis5ddata.tar.Z

2.- Create the folder to be installed in: /usr/local/vis5d $tar -xvzfvis5data.tar.Z

This is created:

EARTH.TOPO LAMPS.v5d OUTLSUPW OUTLUSAM SCHL.v5d vis5d5.1

$tar -xvzf vis5d5.1.tar.Z Figure 69 clone.tcl label.tcl lui5 movie2.tcl README spin.tcl trajcol.tcl contrib highwind.tcl Makefile movie.tcl README.ps src user- funcs convert import listfonts Mesa NOTICE PORTING trajcol2.tcl util wslice. tcl then tovis5d.csh must show the text as follows:

142 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R III

3.2 IMPLEMENTATION OF NUMERICAL MODELS FOR CLIMATE PREDICTION

The Regional Group of Numerical Modeling

The NMSs of the Andean countries have given a written approval to officially belong to a Regional Numerical Mod- eling Group (RMG). This group was created in Guayaquil in June 2008, and it is at the moment under the Techni- cal Coordination of Prof. Angel G. Munoz (attached to the Scientific Modeling Center of The University of Zulia and CIIFEN research associate), and under the institutional coordination of CIIFEN.

The Group constitutes an efficient mechanism to consoli- date the technical capabilities of those who use models in NMSs and thus sustain and improve what is obtained throughout this regional project.

Annex II includes the GRM Reference Terms and letters of support signed by the 6 Directors of the Meteorological Services.

Additionally, a wiki was developed for the installation of the operating system, available at: http://www.cmc.org.ve/wiki/

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CHAPTER IV Implementation of Agro Climatic Risk maps Pilar Ycaza [email protected] CHAPTER IV Nadia Manobanda [email protected]

4.1. DEFINITION OF RISK1 mum temperature in a seasonal period (three months) and is based on the output of the statistical model. These Risk is defined as the combination of the probability of oc- parameters are considered as external factors affecting currence of an event and its negative consequences1. The the crop phenological development, adverse effects of in- factors that comprise it are the threat and vulnerability. creased intensity and frequency with which they produce floods, drought, frost and excessive heat events whose ef- Thread is a phenomenon, substance, human activity or fects are negative for most crops. dangerous condition that can cause death, injury or other health impacts, as well as property damage, loss of live- As internal vulnerability elements of directly proportional lihoods and services, social and economic disruption or crops, we considered that the exposure and susceptibility damage to the environment1. Threat is determined by the of the crop is inversely proportional to its resilience. The intensity and frequency. exposure of the crop was determined considering the location and environmental conditions in which it grows, and Vulnerability the characteristics and circumstances of a that for this case were: climate agriculture floor, season, tex- community, system or asset that make it susceptible to the ture, slope, soil retention capacity, areas prone to erosion, damaging effects of a threat1. flooding, landslides, frost and other specific conditions in the pilot area to determine how much the crop is exposed With the mentioned factors is obtained the following for- to the climate threat. mula. On the other hand, crop resilience is determined by the RISK= THREAD . VULNERABILITY2 degree of weakness in the face of adversity climate at different stages of development; for example in the case of The factors that make up the vulnerability are exposure, corn, high temperatures stop the growth of the plantation, susceptibility and resilience, expressing their relationship during flowering it can suffer more damage because high in the formula. temperatures increase the number of sterile plants and de- creases the number of kernels per cob , i.e. that the climate VULNERABILITY= EXPOSURE . SUSCEPTIBILITY damage leads to reduced growth of crops per hectare and RESILIENCE 2 a reduction in their field.

Exposure is the disadvantaged due to location, position or As the last component and inversely proportional in the location of a subject, object or system at risk. agriculture climate risk measurement is the ability to cope with adverse weather conditions, expressed in this study Susceptibility is the degree of inherent fragility of a sub- by management practices that farmers have to deal with ject, object or system to counter a threat and receive a pos- environmental hazards; an example is the development of sible impact due to the occurrence of an adverse event. drainage and irrigation canals to offset deadly floods.

Resilience is the ability of a system, community or society In conclusion, agriculture climate risk estimation is estab- exposed to a threat to resist, absorb, adapt and recover lished by the relationship of probable climatic effects. This from the effects of timely and effective manner, including is determined by the parameter of precipitation and tem- the preservation and restoration of its basic structures and perature on crops, whose vulnerability is represented by the functions. susceptibility of the crop at different development cycles as well as the ability to cope with adversity represented by 4.2. CONCEPTUAL MATHEMATICAL MODEL farmer’s management practices and its relationship along OF AGRO-CLIMATIC RISK with the crop’s exposure. This is represented mainly by the soil grain size characteristics, the presence of the crop in For agriculture climate risk estimation, the following for- areas of recurrent adverse events such as floods and frost mula was used: 4.3. COMPONENTS AND AGRICULTURE CLI- CROP EXPOSURE MATE RISK VARIABLES SUSCEPTIBILITY AGROCLIMATIC = CLIMATE RISK THREAD CROP RESILENCE Agriculture climate risk components are borrowed from the general formula of risk calculation2, these components be- CROP * ing threat and vulnerability. They are in turn composed by VULNERABILITY exposure, susceptibility and the ability of the crop to face *Vulnerability = [Susceptibility / Resilience]. Exposure. the threat. Each component is described as follows:

The threat is made up of the relation of three climatic parameters: precipitation, maximum temperature and mini- 2.Marti Ezpeleta, A., 1993. Cálculo del Riesgo de Adversidades Climáticas para los Cultivos: Los Cereales de Verano en Monte- negros. p.264 1. UNISDR, Terminology on Disaster Risk Reduction 2009 for the Dpto. de Geografía y Ordenación del Territorio, Universidad Za- concepts of risk, vulnerability and threat. ragoza.

146 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R IV

4.3.1. Thread The areas prone to floods were evaluated as follows:

The most adverse weather threats to crops are extreme FLOOD FREQUENCY VALUE events or persistent rainfall and temperature, with which floods, drought and frost is associated. Very often 5 Often 4 To evaluate the threat, the following values were consid- Regularly 3 ered: precipitation, maximum temperature and minimum Little 2 temperature above and below normal, subject to grada- tions for threat assessment introduced by each. Slightly 1 No 0 These variables were assigned proportionate with the level of prediction, according with what is generated from statis- Table 3.- Evaluation of frequency of flooding. Project ATN/ tical models for seasonal forecasting. OC-10064-RG

The altitude is valued based on a level that divides the up- THREAT SCENARIOS VALUE per zones from the lower zones in the area of interest, as > 50% of normal 5 follows: 50% above normal 4 40% above normal 3 ALTITUDE VALUE 30% above normal 2 High zone 1 Between 10 and 20% above normal 1 Lower zone 2 Normal 0 Table 4.- Evaluation of altitudinal zones to floods. Project Between 10% and 20% below normal 1 ATN/OC-10064-RG 30% below normal 2 40% below normal 3 Frost 50% below normal 4 To evaluate the exposure, altitude and frost-prone areas were considered. Less than 50% of normal 5 Frost-prone areas were evaluated as follows: Table 1.- Evaluation of the climate threat. Project ATN/ OC-10064-RG FREQUENCY OF FROST VALUE 4.3.2. Vulnerability Very often 5 Often 4 As the general formula of vulnerability states2, we calculat- Regularly 3 ed the components of vulnerability, i.e. exposure, susceptibility and resilience, in this way: Little 2 Slightly 1 Exposure No 0

Floods Table 5.- Evaluation of frequency of frost. Project ATN/ To evaluate the exposure we considered soil texture (to OC-10064-RG infer the water-retaining capacity), flood risk areas and altitude. The altitude is valued based on a level that divides the zones of the area of interest, as follows: Depending on the capacity of soil to retain water and considering the texture as the central element related to this ability, the following values were assigned for different tex- ALTITUDE VALUE tural types: High zone 2 Lower zone 1 TEXTURE VALUE Very Fine 5 Tabla 6.- Valoración de pisos altitudinales ante heladas. Proyecto ATN/OC-10064-RG Fine 4

Media 3 Susceptibility Moderately coarse 2 Susceptibility was valued according to the phenological Coarse 1 stage of the crop, for different possible climate conditions predominant development stage in which the crop is found Table 2.- Evaluation of texture. Project ATN/OC-10064-RG for the month or period of interest. The assessment was performed considering the levels of precipitation and temperatures above and below normal.

INTERNATIONAL RESEARCH CENTRE ON EL NIÑO - CIIFEN 147 C H A P T E R IV

The evaluation was estimated considering the team of agricultural experts of the project and the bibliography used [3]. PRECIPITATION STAGE % Above normal Normal Resilience >50 50 40 30 20 10 For the assessment of resilience we considered the irriga- Sowing- tion and drainage infrastructure, whose presence allows Germination 4 4 3 2 1 1 0 crops to reduce the impacts caused by adverse climate Growth- events; it is evaluated as follows: Tillering 5 4 4 3 2 1 0 Flowering 5 5 4 4 3 2 0 Grain filling 5 5 5 4 4 3 0 IRRIGATION AND DRAINAGE INFRASTRUCTURE VALUE Maturation- Presence 1 Harvest 5 5 5 5 5 4 0 Abscence 2

Table 7. Susceptibility rating of phenological phases to Table 11. Assessment of irrigation and drainage infrastruc- above normal rainfall. Project ATN/OC-10064-RG ture. Project ATN/OC-10064-RG

4.4. PROJECT APPLICATION AREAS PRECIPITATION STAGE % Bellow normal Normal For the definition of pilot areas the following criteria were >50 50 40 30 20 10 considered: Sowing- Germination 5 5 5 5 4 4 0 • Existence of an acceptable spatial coverage of meteoro-

Growth- 5 5 5 4 4 3 0 logical stations. Tillering • Agricultural activity relevant in social and economic terms. Flowering 5 5 4 4 3 2 0 • Farming activity with a level of vulnerability. Grain filling 5 4 4 3 2 1 0 • Available information base. Maturation- Harvest 4 4 3 2 1 1 0 The pilot areas designated for the project by each of the countries with the selection of crops, are shown in the table Table 8. Susceptibility rating of phenological stages to below. below normal rainfall. Project ATN/OC-10064-RG

TEMPERATURES STAGE % Above normal Normal >50 50 40 30 20 10 Sowing- Germination 4 4 3 2 1 1 0 Growth- Tillering 5 5 4 3 3 2 0 Flowering 5 5 4 4 3 3 0 Grain filling 5 5 4 3 3 2 0 Maturation- Harvest 4 4 3 2 1 1 0

Table 9. Susceptibility rating of phenological stages at temperatures above normal. Project ATN/OC-10064-RG

TEMPERATURES STAGE % Bellow normal Normal >50 50 40 30 20 10 Sowing- Germination 5 5 4 4 3 3 0 Growth- Tillering 5 5 4 3 3 2 0 Flowering 5 5 4 3 3 2 0 Grain filling 4 3 3 2 1 1 0 Maturation- Harvest 3 3 2 2 1 1 0

Table 10. Susceptibility rating of phenological stages at temperatures below normal. Project ATN/OC-10064-RG

148 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R IV

Country Pilot Area Crops

Venezuela Portuguesa State Rice, corn, sesame, sorghum Colombia Bogota and Tolima Flowers, rice Ecuador Guayas, Manabí, Los Ríos Corn, rice, soybeans Perú Mantaro Valley Potato, corn, artichoke Bolivia Highland Region Potatoes, lima beans, quinoa Chile Valparaíso Region Citrus, avocado

Table 12. Pilot Areas and Crops for Country. Project ATN/OC-10064-RG

Figure 70. Pilot Areas of Project ATN/OC-10064-RG.

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4.5. INFORMATION REQUIREMENTS 4.5.3. Thematic mapping

Information was requested in tabular format for qualitative Thematic information, that serves as input for this calcula- and quantitative information related mainly to agro-eco- tion, should be obtained in digital format [shapefile] and be logical characteristics of crops and in digital format [shape- properly geo-referenced. file] for the base and thematic mapping.

4.5.1. Agro-ecological Thematic Mapping Floods Erosion The data required as inputs to evaluate the crops consider- Landslides ing their agro-ecological characteristics are: Droughts • Predominant varieties. Frost • Phenological stages. • Threshold precipitation [mm]. Current land use • Threshold temperature [° C]. Vegetal cover • Annual Season. • Soil Texture. Soil texture Crop location We worked with the dominant variety that was the most representative of the crop in the area, and the precipita- Table 16. Requirements for thematic mapping. Project tion and temperature thresholds that are required to find ATN/OC-10064-RG the optimal conditions for crop development. We included other requirements as the periods of the year for planting The forecasts of precipitation, maximum and minimum and harvesting and optimal soil texture. An example of temperature, are generated by NMHSs and must be con- agro-ecological requirements is shown in Table 14. verted to digital format and georeferenced.

REQUIREMENTS AND AGRO-ECOLOGICAL PARAMETERS Climate Precipitation forecast Country Information Maximum temperature forecast Pilot zone Minimum temperature forecast Cultivation Predominant varieties Table 17. Requirements of climate forecasting. Project ATN/OC-10064-RG Phenological stages Threshold precipitation (mm) From: To: It requires for the satellite images of the area of interest to be updated, preferably within the last two years. The best Threshold temperature (ºC) From: To: image resolution required is10 m. Economic threshold (%)

Annual season Table 18. Requirement Satellite Soil texture Area of of satellite images. Images interest Project ATN/OC- Table 14. Requirements and agro-ecological parameters. 10064-RG Project ATN/OC-10064-RG 4.5.4. Treatment of Information 4.5.2. Base mapping The information collected must go through a validation Digitalized basic information was required to put together process, correction, editing and standardization, which is the base mapping. This information had to have official sta- known as information processing. In this process all the tus and be as updated as possible. errors and discrepancies that exist on the provided information are corrected. It was necessary to standardize the Basic Mapping National Political Limit layers to the same reference and projection system [WGS 84 - UTM]. Provincial or Departmental Political Boundaries Municipal and Cantonal Political Boundaries 4.5.5. Soil and climatic characteristics in pilot Water System areas Road System Populated Centers As a result of data gathering, the edapho-climatic charac- Urban Areas terization was also obtain in each area. Contours Topography

Table 15. Requirements of basic cartography. Project ATN/OC-10064-RG

150 TECHNICAL GUIDE FOR THE IMPLEMENTATION OF A REGIONAL CLIMATE INFORMATION SYSTEM APPLIED TO AGRICULTU- RAL RISK MANAGEMENT IN THE ANDEAN COUNTRIES C H A P T E R IV

Venezuela