4-27 Acronyms for 2021 Review
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Gridded Model Output Statistics: Improving and Expanding
6A.6 GRIDDED MODEL OUTPUT STATISTICS: IMPROVING AND EXPANDING Kathryn K. Gilbert*, Bob Glahn, Rebecca L. Cosgrove1, Kari L. Sheets, Geoffrey A. Wagner2 Meteorological Development Laboratory National Weather Service, NOAA Silver Spring, MD 1National Center for Environmental Prediction, National Weather Service, Camp Springs, MD 2Wyle Information Systems, McLean, VA 1. INTRODUCTION Since the original implementation, gridded MOS has undergone several adaptations to improve For years, National Weather Service (NWS) consistency as well as regional applicability. MDL forecasters have used Model Output Statistics developers have welcomed assessments of grid- (MOS) guidance (Glahn and Lowry 1972) as an ded MOS from local forecasters in order to make aid in producing text forecast products issued to adjustments to provide guidance which is both the user community. However, the methods fore- representative of the local area, and compatible casters use to generate products have changed with the process for generating forecasts for because of requirements to produce the official NDFD. Some of the improvements resulting from NWS 7-day forecasts on high-resolution grids. forecaster feedback include: removal of unrepre- These gridded, or digital, forecasts are put into a sentative forecasts from the analysis, expansion of National Digital Forecast Database (NDFD; Glahn the grid coverage, and modifications to the and Ruth 2003). NWS forecasters need support- land/water mask. ing guidance available on a grid, at a resolution comparable to the grid resolution used at the local In this paper, we discuss the current state of forecast office. One approach to the problem of the gridded MOS suite, as well as ongoing en- providing guidance on the required grid is to objec- hancements to the operational GFS-based MOS tively analyze forecasts at MOS stations. -
Using Earth Observation Data to Improve Health in the United States Accomplishments and Future Challenges
a report of the csis technology and public policy program Using Earth Observation Data to Improve Health in the United States accomplishments and future challenges 1800 K Street, NW | Washington, DC 20006 Tel: (202) 887-0200 | Fax: (202) 775-3199 Author E-mail: [email protected] | Web: www.csis.org Lyn D. Wigbels September 2011 ISBN 978-0-89206-668-1 Ë|xHSKITCy066681zv*:+:!:+:! a report of the csis technology and public policy program Using Earth Observation Data to Improve Health in the United States accomplishments and future challenges Author Lyn D. Wigbels September 2011 About CSIS At a time of new global opportunities and challenges, the Center for Strategic and International Studies (CSIS) provides strategic insights and bipartisan policy solutions to decisionmakers in government, international institutions, the private sector, and civil society. A bipartisan, nonprofit organization headquartered in Washington, D.C., CSIS conducts research and analysis and devel- ops policy initiatives that look into the future and anticipate change. Founded by David M. Abshire and Admiral Arleigh Burke at the height of the Cold War, CSIS was dedicated to finding ways for America to sustain its prominence and prosperity as a force for good in the world. Since 1962, CSIS has grown to become one of the world’s preeminent international policy institutions, with more than 220 full-time staff and a large network of affiliated scholars focused on defense and security, regional stability, and transnational challenges ranging from energy and climate to global development and economic integration. Former U.S. senator Sam Nunn became chairman of the CSIS Board of Trustees in 1999, and John J. -
Verification of Model Output Statistics Forecasts Associated with the North
Allison Monarski, University of Maryland Masters Scholarly Paper, December 6, 2011 1 Department of Atmospheric and Oceanic Science Verification of Model Output Statistics forecasts associated with the North American Mesoscale model upgrade Results of work done during the Student Career Experience Program tour with the Statistical Modeling Branch (SMB) of the Meteorological Development Lab (MDL) of the National Weather Service (NWS), Silver Spring, MD, under the guidance of Mark Antolik & Kathy Gilbert, NWS/MDL/SMB I. Introduction and Motivation For the past four decades, the model output statistics (MOS) technique has been in operation and forecasters and meteorologists globally have used forecasts generated through MOS. MOS is a statistical approach to forecasting which involves determination of a relationship between a predictand and numerical weather prediction (NWP) model output for a variety of variables (predictors) at the same projection times (Glahn and Lowry 1972). Through the years, MOS has provided reliable forecasts, predicting quantities that the underlying NWP model is unable to predict. Specifically, the MOS technique can account for local effects on certain elements that the coarser resolution NWP model cannot detect (Antolik and Baker 2009). Usually, statistical forecasting systems, such as MOS, rely on many years of stable development data for optimal performance. However, due to frequent changes in the underlying structure, physics, and dynamics of the NWP models, this kind of data is difficult to obtain. Fortunately, developers at the National Weather Service’s (NWS) Meteorological Development Laboratory (MDL) have been able to show that reliable MOS forecasts can be produced using as little as just two years of data from models which are evolving (Antolik and Baker 2009). -
Snow Modeling and Observations at NOAA's National Operational
Snow Modeling and Observations at NOAA’S National Operational Hydrologic Remote Sensing Center Thomas R. Carroll National Operational Hydrologic Remote Sensing Center, National Weather Service, National Oceanic and Atmospheric Administration Chanhassen, Minnesota, USA Abstract The National Oceanic and Atmospheric Administration’s (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous United States. The NWP model forcings are physically downscaled from their native 13 kilometer2 (km) spatial resolution to a 1 km2 resolution for the coterminous United States. The downscaled NWP forcings drive the NOHRSC Snow Model (NSM) that includes an energy-and-mass-balance snow accumulation and ablation model run at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the model state variables simulated by the NSM using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the NSM, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The NSM is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA’s NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the information necessary and available to produce a “best estimate” of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. -
Model Output Statistics Cascade to Improve Day Ahead Solar Irradiance Forecast
Available online at www.sciencedirect.com ScienceDirect Solar Energy 117 (2015) 99–113 www.elsevier.com/locate/solener Model output statistics cascade to improve day ahead solar irradiance forecast M. Pierro a, F. Bucci a, C. Cornaro a,⇑, E. Maggioni b, A. Perotto b, M. Pravettoni c, F. Spada b a Department of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico, 1, 00133 Rome, Italy b IDEAM srl, Via Frova 34, Cinisello Balsamo, MI, Italy c Institute of Applied Sustainability to the Build Environment, University of Applied Sciences and Arts of Southern Switzerland, Canobbio CH-6952, Switzerland Received 29 September 2014; received in revised form 9 April 2015; accepted 26 April 2015 Communicated by: Associate Editor Christian A. Gueymard Abstract In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. -
GST Responses to “Questions to Inform Development of the National Plan”
GST Responses to “Questions to Inform Development of the National Plan” Name (optional): Dr. Darrel Williams Position (optional): Chief Scientist, (240) 542-1106; [email protected] Institution (optional): Global Science & Technology, Inc. Greenbelt, Maryland 20770 Global Science & Technology, Inc. (GST) is pleased to provide the following answers as a contribution towards OSTP’s effort to develop a national plan for civil Earth observations. In our response we provide information to support three main themes: 1. There is strong science need for high temporal resolution of moderate spatial resolution satellite earth observation that can be achieved with cost effective, innovative new approaches. 2. Operational programs need to be designed to obtain sustained climate data records. Continuity of Earth observations can be achieved through more efficient and economical means. 3. We need programs to address the integration of remotely sensed data with in situ data. GST has carefully considered these important national Earth observation issues over the past few years and has submitted the following RFI responses: The USGS RFI on Landsat Data Continuity Concepts (April 2012), NASA’s Sustainable Land Imaging Architecture RFI (September 2013), and This USGEO RFI (November 2013) relative to OSTP’s efforts to develop a national plan for civil Earth observations. In addition to the above RFI responses, GST led the development of a mature, fully compliant flight mission concept in response to NASA’s Earth Venture-2 RFP in September 2011. Our capacity to address these critical national issues resides in GST’s considerable bench strength in Earth science understanding (Drs. Darrel Williams, DeWayne Cecil, Samuel Goward, and Dixon Butler) and in NASA systems engineering and senior management oversight (Drs. -
Carbon Earth Observatory for Carbon Dioxide Reduction Robert D
Carbon Earth Observatory for Carbon Dioxide Reduction Robert D. Cormia Foothill College GHG Emissions / NET Strategies Terrestrial Options for Negative Emissions Earth System Observation Data Platform Technology (NET) The messaging from IPCC is clear; without significant and sustained • Argo • OCO-2/OCO-3 Carbon Dioxide Reduction (CDR) strategies, there is no realistic • Afforestation and reforestation, stop deforestation, • Aqua • GOSAT 2 chance of avoiding potentially disastrous climate change. increase biomass of forest and soils for decades In addition to emission reduction, “drawdown” of atmospheric • Terra • ECOSTRESS carbon dioxide must begin soon and remain in place through the • Monitor and enhance grassland productivity and • CloudSat • GEDI end of the century. There are carbon sinks in the terrestrial carbon sequestration, including hydrology • CALIPSO • LandSat biosphere that have the potential to remove gigatons of carbon Improve soil microbial activity, carbon uptake in soils, dioxide each year, for decades or more. An earth observatory • • SMAP • TROPOMI system, for analysis of carbon cycle processes throughout the userecommended management practices • ICESat-2 • GeoCARB (2022) biosphere, could help measure, inform, and optimize terrestrial • Restore wetlands and connect to ocean inlet to increase carbon sequestration projects. salinity and decrease methane emissions NASA Earth Observing System (EOS) • Enhance Net Primary Productivity (NPP) of oceans Integrated toolset to help achieve CDR Goals NASA’s Earth Observatory tools are designed for accurate and precise measurements of atmospheric gases, geometric aspects of land and biomass, and can sense biochemical changes in plants and biomass that may result from climate change. If we are to be effective in optimizing carbon dioxide reduction projects, we need an integrated data platform with both spatial and temporal resolution. -
Supporting Gridded Model Output Statistics Forecast Guidance System
Supporting Gridded Model Output Statistics Forecast Guidance System Kari L. Sheets ABSTRACT The Meteorological Development Laboratory (MDL) of NOAA’s National Weather Service is creating a National Digital Guidance Database at a fine resolution to comple- ment the existing National Digital Forecast Database. To help accomplish this goal, MDL is producing a gridded Model Output Statistics (MOS) forecast guidance system. Currently, gridded MOS populates a 5-km grid with elements needed for weather fore- cast grids covering the contiguous United States. This paper describes the use of Geographic Information System (GIS) to gener- ate and quality control geophysical variables, create a station dictionary including land/water designations for the observing stations, and analyze or troubleshoot problem areas in gridded MOS weather elements. Stations used in traditional MOS development are unevenly distributed, leaving developers searching for additional data to aid in ana- lyzing these values to the aforementioned grid. GIS is integral in supplying the data to move from station-based MOS to gridded MOS. 1. Introduction The Meteorological Development Laboratory (MDL) of NOAA’s National Weather Service (NWS) is developing a National Digital Guidance Database (NDGD) at fine reso- lutions to complement the existing National Digital Forecast Database (NDFD, Glahn and Ruth 2003). To help accomplish this goal, MDL is creating a gridded forecast guid- ance system. Current forecast guidance is produced for the United States and its terri- tories at approximately 1800 hourly observing sites and over 5000 cooperative observ- ing sites by using the Model Output Statistics (MOS) technique (Glahn and Lowry 1972). In the MOS approach, observed predictand data are statistically related to predictors such as forecasts from dynamical models, surface observations, and geoclimatic infor- mation. -
Statistical Post-Processing of Ensemble Forecasts of Temperature in Santiago De Chile
Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile Mailiu D´ıaza, Orietta Nicolisa;b, Julio Cesar´ Mar´ınc and Sandor´ Barand aDepartment of Statistics, University of Valpara´ıso Gran Breta~na1111, Valpara´ıso,Chile bFaculty of Engineering, Andres Bello University Quillota 980, Vi~nadel Mar, Chile cDepartment of Meteorology, University of Valpara´ıso Gran Breta~na644, Valpara´ıso,Chile dFaculty of Informatics, University of Debrecen Kassai ´ut26, H-4028 Debrecen, Hungary Abstract Currently all major meteorological centres generate ensemble forecasts using their operational ensemble prediction systems; however, it is a general problem that the spread of the ensemble is too small, resulting in underdispersive forecasts, leading to a lack of calibration. In order to correct this problem, different statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques are tested for calibrating arXiv:1809.04042v1 [stat.AP] 11 Sep 2018 9 member ensemble forecast of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting (WRF) model using different planetary boundary layer and land surface model parametrization. In particular, the ensemble model out- put statistics (EMOS) and Bayesian model averaging techniques are implemented and since the observations are characterized by large altitude differences, the estimation of model parameters is adapted to the actual conditions at hand. Compared to the raw ensemble all tested post-processing approaches significantly improve the calibration of probabilistic and the accuracy of point forecasts. The EMOS method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill. -
Complete List of Contents
Complete List of Contents Volume 1 Cape Canaveral and the Kennedy Space Center ......213 Publisher’s Note ......................................................... vii Chandra X-Ray Observatory ....................................223 Introduction ................................................................. ix Clementine Mission to the Moon .............................229 Preface to the Third Edition ..................................... xiii Commercial Crewed vehicles ..................................235 Contributors ............................................................. xvii Compton Gamma Ray Observatory .........................240 List of Abbreviations ................................................. xxi Cooperation in Space: U.S. and Russian .................247 Complete List of Contents .................................... xxxiii Dawn Mission ..........................................................254 Deep Impact .............................................................259 Air Traffic Control Satellites ........................................1 Deep Space Network ................................................264 Amateur Radio Satellites .............................................6 Delta Launch Vehicles .............................................271 Ames Research Center ...............................................12 Dynamics Explorers .................................................279 Ansari X Prize ............................................................19 Early-Warning Satellites ..........................................284 -
Visibility Forecasts from the RUC20
5.10 Visibility Forecasts from the RUC20 Tracy Lorraine Smith*, Stanley G. Benjamin, and John M. Brown NOAA Research – Forecast Systems Laboratory Boulder, CO 80305 USA *Also affliliated with the Cooperative Institue for Research in the Atmosphere * email – [email protected] 1. Introduction 2002. The primary model changes in the RUC20 include a new Grell convective parameterization, At least five characteristics of the new 20-km explicit clouds using mixed phase microphysics, Rapid Update Cycle (RUC20, Benjamin et al. an update to the RUC/MM5 Reisner level-4 2002a, this volume), being implemented at NCEP mixed-phase microphysics developed jointly by in April 2002, should contribute to improvements NCAR and FSL, and new land-surface in visibility forecasts. These include higher processes. RUC20 also assimilates GOES horizontal and vertical resolution (from 40 km to cloud-top data to assist in the description of initial 20 km, 40 to 50 levels), improved versions of the cloud/hydrometeor fields. The smaller grid size MM5/RUC mixed-phase cloud microphysics and also allows the RUC20 to resolve smaller areas RUC land-surface schemes, assimilation of of clouds and precipitation, which should benefit GOES cloud information modifying RUC 1-h visibility diagnoses. A more accurate diurnal hydrometeor forecasts (Benjamin et al. 2002b), cycle with a more frequent call to the shortwave and an improved RUC visibility algorithm radiation module should also make a minor (Smirnova et al. 2000), which was originally contribution toward diagnosis of fog/low-level based on the Stoelinga-Warner method. clouds. Ongoing 3-h verification against METAR visibility observations is being conducted for forecasts The visibility field from the RUC is output from a from both the RUC2 (called RUC40 in this paper) visibility translation algorithm that uses near- and the RUC20 to assess the impact of the surface hydrometeor (cloud water, rain water, changes. -
A North American Hourly Assimilation and Model Forecast Cycle: the Rapid Refresh
APRIL 2016 B E N J A M I N E T A L . 1669 A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh STANLEY G. BENJAMIN,STEPHEN S. WEYGANDT,JOHN M. BROWN,MING HU,* CURTIS R. ALEXANDER,* TATIANA G. SMIRNOVA,* JOSEPH B. OLSON,* ERIC P. JAMES,* 1 DAVID C. DOWELL,GEORG A. GRELL,HAIDAO LIN, STEVEN E. PECKHAM,* 1 TRACY LORRAINE SMITH, WILLIAM R. MONINGER,* AND JAYMES S. KENYON* NOAA/Earth System Research Laboratory, Boulder, Colorado GEOFFREY S. MANIKIN NOAA/NWS/NCEP/Environmental Modeling Center, College Park, Maryland (Manuscript received 8 July 2015, in final form 8 December 2015) ABSTRACT The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision- making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC.