DOCSLIB.ORG

Reanalysis Before Radiosondes Using Ensemble Data Assimilation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Reanalysis Before Radiosondes Using Ensemble Data Assimilation J2.1 REANALYSIS BEFORE RADIOSONDES USING ENSEMBLE DATA ASSIMILATION Jeffrey S. Whitaker∗ ,Gilbert P. Compo, Xue Wei and Thomas M. Hamill NOAA-CIRES Climate Diagnostics Center, Boulder, CO 1. INTRODUCTION observing network, created by sub-sampling the surface pressure observations for 2001. We focus on surface pres- A primary goal of climate change research is to under- sure observations since they are the most widely available stand variations in the frequency and intensity of severe and reliable observations in the early 20th century, and weather events on decadal and longer time-scales. An provide more information about the state of the free tropo- obvious prerequisite for achieving this goal is an accu- sphere than surface wind and temperature observations. rate baseline estimate of the frequency and intensity of In a companion report we will carefully examine the avail- severe weather over the last century. Analyses of long- able data record for the last 150 years and assess the per- term changes in extra-tropical cyclone frequency and in- formance of several analysis schemes with surface-only tensity have been hampered by the inadequacy of cur- observation networks representative of 1890 to 1940. rent datasets (IPCC 2001, p. 163). Since synoptic-scale weather systems have time-scales of less than a week, Previous studies using idealized ensemble data assimi- a century-long dataset of tropospheric circulation fields at lation systems (e.g. Hamill and Snyder 2000) have shown daily resolution is required. The NCEP-NCAR 50-year re- that their flow-dependent background-error covariances analysis (Kistler and Coauthors 2001) provides four times are most beneficial when the observing network is sparse. daily gridded circulation fields beginning in 1948, when When observations are very dense, the background-error digital upper-air observations were widely available. The covariances do not change as much from time to time, only daily tropospheric circulation dataset available that so static background-error covariance models (such as extends back before 1948 is derived from charts of sea- used in 3DVar) can be nearly as effective. In addi- level pressure hand-drawn by U.S. Air Force meteorolo- tion, the computational cost of computing analysis incre- gists in the 1940’s and 1950’s (United States Weather Bu- ments in recently proposed ensemble-data assimilation al- reau 1944). Although a remarkable achievement for its gorithms (Houtekamer and Mitchell 2001; Whitaker and time, this original reanalysis suffers from serious problems Hamill 2002) is directly proportional to the number of ob- associated with incorrect assumptions made by the ana- servations being assimilated. Therefore, ensemble-based lysts in data sparse regions (Jones 1987; Trenberth and data assimilation should be more computationally feasible Paolino 1980) and does not provide estimates of the full and provide the greatest benefit over current operational three-dimensional tropospheric structure. Clearly, a bet- schemes in situations when observations are sparse. Re- ter dataset is needed - but is it possible to create a more analysis before the radiosonde era is just such a situation. accurate daily tropospheric circulation dataset for the first half of the 20th century given the paucity of available ob- The paper is organized as follows: section 2 contains servations? a description of the experimental design, including the In this study we examine whether advanced data assim- simulation of the 1915 observing network, the ensemble ilation systems have significant advantages over currently data assimilation system and forecast model. Section 3 available systems for sparse networks of surface pressure presents the results of the assimilation experiments, which observations representative of the early part of the 20th show that the EnSRF can produce mid-tropospheric anal- century. Specifically, we examine the performance of the yses given surface observations at 1915 densities which ensemble-based data assimilation system described by are as accurate as 2.5 day forecasts are today. Section 4 Whitaker and Hamill (2002) applied to a simulated 1915 contains a summary of the results and a discussion of the unresolved issues that need to be addressed before these ∗Corresponding author address: Jeffrey S. Whitaker, NOAA Climate Diagnostics Center, R/CDC1, 325 Broadway, Boulder, CO 80305; email: techniques can be applied to a real reanalysis of the first [email protected] half of the 20th century. 2. Experimental Design assimilated, they were used to reduce the surface pres- sure observation to the model orography as discussed in a. The Observations the next paragraph). A map illustrating a typical simulated surface pressure network at 00 and 12 UTC is shown in To simulate how a modern data assimilation system can the bottom hand panel of Figure 4. In this example there be expected to perform on a historical observational net- are 204 surface pressure and temperature observations in work, the 2001 observational network was reduced to only the Northern Hemisphere poleward of 20oN. At 06 and 18 surface observations with a density typical of 1915. The UTC, the number of surface marine observations is nearly number of synoptic observations potentially available for the same, but there are almost no observations over land each month during the period 1913-1917 was determined areas. in 5x5 degree boxes from a detailed inventory of the digital land surface data holdings of the National Center for At- mospheric Research (NCAR), the National Climatic Data Center (NCDC), the Waves and Storms dataset (Schmith et al. 1997), and manuscript data holdings of NCDC. The Global Historical Climate Network (GHCN) surface pres- sure station locations were used a proxy for synoptic re- ports currently available only in manuscript form, some of which are now being digitized by NCDC (S. Doty, per- sonal communication), Environment Canada (V. Swail, personal communication), the European Union (P. Jones, personal communication) as well as other international ef- forts (R. Jenne, personal communication). The marine ob- servations available were also determined in 5x5 degree boxes from a detailed inventory of ICOADS Release 2.0 Figure 1: Number of surface pressure observations (per (Woodruff et al. 1998; Diaz et al. 2002), the German Ma- day) available in the Northern Hemisphere poleward of rine Meteorological archive (courtesy of V. Wagner), and 20oN each year from all available digital sources, including the Kobe Collection 2001 (Manabe 1999). The 1915 ob- those currently being digitized. servation network was chosen for this study since it is rep- resentative of data availability during the earlier part of Observational error standard deviations were the same the 20th century. The number of surface observations in- as those used in the NCEP-NCAR reanalysis, 1.6 hPa for creases dramatically in the 1930’s and 1940’s. ship observations and 1 hPa for land stations. In situations The quality-controlled observations used as input to the where the absolute difference between the model orog- NCEP-NCAR reanalysis (Kistler and Coauthors 2001) for raphy and the real orography is less than 600 meters at 2001 were sub-sampled to simulate the 1915 network. the observation location, and a co-located temperature ob- The 2001 observations were first reduced by retaining only servation is available, the surface pressure observation is surface pressure observations from radiosonde and ma- reduced to the model orography assuming the mean tem- 1 Γ∆ rine reports issued within 30 minutes of the analysis time. perature in the intervening layer is Tob+ 2 z, where Tob is o 1 The location of the radiosonde stations gives an excel- the temperature observation, Γ is 6.5 Kkm− and ∆z is lent approximation to the location of historically available the difference between the model and− real orography. The land surface pressure stations. This reduced the total net- observation error is adjusted accordingly, assuming that o 1 work from over 150,000 observations to less than 2000 per the error in the estimate of the lapse rate Γ is 3 Kkm− . analysis. The simulated historical network was then con- If ∆z > 600 m then the surface pressure observation is structed by randomly selecting from the reduced network not| used.| If a co-located temperature observation is not in each five-degree box with a probability equal to the av- available, the surface pressure observation is used with- erage number of daily historical surface pressure observa- out modification if ∆z < 10 m, otherwise it is not used. tions in the box normalized by the average number of daily To assess the benefit| | of flow dependent background- surface pressure observations in the reduced 2001 net- error covariances, the analyses produced by the ensemble work. Figure 1 shows a map of the probabilities assigned data assimilation system are compared to those produced to each five-degree box. Surface temperature observa- by two other simpler systems with static background-error tions were included with each surface pressure observa- covariance estimates. The 3DVar system used to produce tion (although surface temperature observations were not the NCEP-NCAR reanalysis, a.k.a the Climate Data As- 2 similation System, or CDAS (Kistler and Coauthors 2001), with the NCEP-NCAR reanalysis for 2001. Because the was adapted to the 1915 observation network by multiply- reanalysis used several orders of magnitude more obser- ing the background-error covariances used in the reanaly- vations, including radiosondes, aircraft and satellite sound- sis by a constant factor > 1, and turning off the divergence ings, we expect that this difference is significantly larger tendency constraint. We call this modified CDAS system than the error in the reanalysis itself. CDAS-SFC. Increasing the background-error covariance amplitude b. The Ensemble Data Assimilation System in the CDAS-SFC system was necessary since the Ensemble data assimilation systems transform a fore- background-error covariances used in CDAS were tuned cast ensemble into an analysis ensemble with appropri- to the modern observing network (Kistler and Coauthors ate statistics.
Load more

Recommended publications
  • AN INTRODUCTION to DATA ASSIMILATION the Availability Of
    AN INTRODUCTION TO DATA ASSIMILATION AMIT APTE Abstract. This talk will introduce the audience to the main features of the problem of data assimilation, give some of the mathematical formulations of this problem, and present a specific example of application of these ideas in the context of Burgers' equation. The availability of ever increasing amounts of observational data in most fields of sciences, in particular in earth sciences, and the exponentially increasing computing resources have together lead to completely new approaches to resolving many of the questions in these sciences, and indeed to formulation of new questions that could not be asked or answered without the use of these data or the computations. In the context of earth sciences, the temporal as well as spatial variability is an important and essential feature of data about the oceans and the atmosphere, capturing the inherent dynamical, multiscale, chaotic nature of the systems being observed. This has led to development of mathematical methods that blend such data with computational models of the atmospheric and oceanic dynamics - in a process called data assimilation - with the aim of providing accurate state estimates and uncertainties associated with these estimates. This expository talk (and this short article) aims to introduce the audience (and the reader) to the main ideas behind the problem of data assimilation, specifically in the context of earth sciences. I will begin by giving a brief, but not a complete or exhaustive, historical overview of the problem of numerical weather prediction, mainly to emphasize the necessity for data assimilation. This discussion will lead to a definition of this problem.
    [Show full text]
  • Radar Data Assimilation
    Radar Data Assimilation David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Radar Data Assimilation (for analysis and prediction of convective storms) David Dowell Assimilation and Modeling Branch NOAA/ESRL/GSD, Boulder, CO Acknowledgment: Warn-on-Forecast project Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL Atmospheric Data Assimilation Definition: using all available information – observations and physical laws (numerical models) – to estimate as accurately as possible the state of the atmosphere (Talagrand 1997) Applications: 1. Initializing NWP models NOAA NCEP, NCAR RAL 2. Diagnosing atmospheric processes (analysis) Schultz and Knox 2009 Assimilating a Radar Observation radar observation (Doppler velocity, reflectivity, …) gridded model fields (wind, temperature, What field(s) should the radar ob. should affect? pressure, humidity, By how much? And how far from the ob.? rain, snow, …) determined by background error covariances (b.e.c.) Various methods have been developed for estimating and using b.e.c.: 3DVar, 4DVar, EnKF, hybrid, … Most
    [Show full text]
  • 5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model
    5B.2 4-Dimensional Variational Data Assimilation for the Weather Research and Forecasting Model Xiang-Yu Huang*1, Qingnong Xiao1, Xin Zhang2, John Michalakes1, Wei Huang1, Dale M. Barker1, John Bray1, Zaizhong Ma1, Tom Henderson1, Jimy Dudhia1, Xiaoyan Zhang1, Duk-Jin Won3, Yongsheng Chen1, Yongrun Guo1, Hui-Chuan Lin1, Ying-Hwa Kuo1 1National Center for Atmospheric Research, Boulder, Colorado, USA 2University of Hawaii, Hawaii, USA 3Korean Meteorological Administration, Seoul, South Korea 1. Introduction The 4D-Var prototype was built in 2005 and has under continuous refinement since then. Many single observation experiments have been carried out to The 4-dimensional variational data assimilation validate the correctness of the 4D-Var formulation. A (4D-Var) (Le Dimet and Talagrand, 1986; Lewis and series of real data experiments have been conducted to Derber, 1985) has been pursued actively by research assess the performance of the 4D-Var (Huang et al. community and operational centers over the past two th 2006). Another year of fast development of 4D-Var has decades. The 5 generation Pennsylvania State led to the completion of a basic system, which will be University – National Center for Atmospheric Research described in section 3. mesoscale model (MM5) based 4D-Var (Zou et al. 1995; Ruggiero et al. 2006), for example, has been widely used for more than 10 years. There are also 2. The WRF 4D-Var Algorithm successful operational implementations of 4D-Var (e.g. Rabier et al. 2000). The WRF 4D-Var follows closely the incremental The 4D-Var technique has a number of advantages 4D-Var formulation of Courtier et al.
    [Show full text]
  • Assimilation of GOES-16 Radiances and Retrievals Into the Warn-On-Forecast System
    MAY 2020 J O N E S E T A L . 1829 Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System THOMAS A. JONES,PATRICK SKINNER, AND NUSRAT YUSSOUF Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, and University of Oklahoma, Norman, Oklahoma Downloaded from http://journals.ametsoc.org/mwr/article-pdf/148/5/1829/4928277/mwrd190379.pdf by NOAA Central Library user on 11 August 2020 KENT KNOPFMEIER AND ANTHONY REINHART Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma XUGUANG WANG University of Oklahoma, Norman, Oklahoma KRISTOPHER BEDKA AND WILLIAM SMITH JR. NASA Langley Research Center, Hampton, Virginia RABINDRA PALIKONDA Science Systems and Applications, Inc., Hampton, Virginia (Manuscript received 14 November 2019, in final form 28 January 2020) ABSTRACT The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-mm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019.
    [Show full text]
  • Recent Results of Observation Data Denial Experiments
    Recent results of observation data denial experiments Weather Science Technical Report 641 24th February 2021 Brett Candy, James Cotton and John Eyre www.metoffice.gov.uk © Crown Copyright 2021, Met Office Contents Contents ............................................................................................................................... 1 1 Introduction .................................................................................................................... 2 2 Operational NWP configuration ...................................................................................... 3 3 Data Denial Experiments ............................................................................................... 6 3.1 Introduction ......................................................................................................... 6 3.2 Results ................................................................................................................ 8 3.3 Continued Impact of POES............................................................................... 12 3.4 Verification of Tropical Cyclone Tracks .............................................................. 15 3.5 A Data Denial Experiment including withdrawal from the ensemble ................... 16 4 FSOI Results ............................................................................................................... 18 5 Conclusions ................................................................................................................. 21 Acknowledgements
    [Show full text]
  • Convective Scale Data Assimilation and Nowcasting
    Convective Scale Data Assimilation and Nowcasting Susan P Ballard1, Bruce Macpherson2, Zhihong Li1, David Simonin1, Jean-Francois Caron1, Helen Buttery1, Cristina Charlton-Perez1, Nicolas Gaussiat2, Lee Hawkness-Smith1 ,Chiara Piccolo2, Graeme Kelly1, Robert Tubbs1, Gareth Dow2 and Richard Renshaw2 1 Met Office, Dept of Meteorology, University of Reading, RG6 6BB [email protected] 2 Met Office, FitzRoy Road, Exeter, EX31 3PB Abstract Increasing availability of computer power and nonhydrostatic models has made limited area NWP at convective scales, 1-4km resolution, a reality for National Met Services in the past few years. At this time around the world nudging, variational data assimilation and ensemble Kalman filters are being used or developed for high resolution data assimilation in research centres and weather services, and are already operational in some Weather Services, for high resolution models in the range 1-10km. This paper reviews some of the issues relating to convective scale limited area data assimilation and in particular their application in NWP-based nowcasting. 1. Introduction Increasing availability of computer power and nonhydrostatic models has made limited area NWP at convective scales, 1-4km resolution, a reality for National Met Services in the past few years. Many services are already using these systems operationally for short-range forecasting up to about T+36hours every 3 or 6hours (Honda et al 2005, Saito et al 2006, Stephan et al 2008, Seity et al 2011, Brousseau et al 2011, Bauer et al 2011). These forecasts are also used for merging with traditional nowcasting techniques to extend the skilful forecast range e.g.
    [Show full text]
  • TC Modelling and Data Assimilation
    Tropical Cyclone Modeling and Data Assimilation Jason Sippel NOAA AOML/HRD 2021 WMO Workshop at NHC Outline • History of TC forecast improvements in relation to model development • Ongoing developments • Future direction: A new model History: Error trends Official TC Track Forecast Errors: • Hurricane track forecasts 1990-2020 have improved markedly 300 • The average Day-3 forecast location error is 200 now about what Day-1 error was in 1990 100 • These improvements are 1990 2020 largely tied to improvements in large- scale forecasts History: Error trends • Hurricane track forecasts have improved markedly • The average Day-3 forecast location error is now about what Day-1 error was in 1990 • These improvements are largely tied to improvements in large- scale forecasts History: Error trends Official TC Intensity Forecast Errors: 1990-2020 • Hurricane intensity 30 forecasts have only recently improved 20 • Improvement in intensity 10 forecast largely corresponds with commencement of 0 1990 2020 Hurricane Forecast Improvement Project HFIP era History: Error trends HWRF Intensity Skill 40 • Significant focus of HFIP has been the 20 development of the HWRF better 0 Climo better HWRF model -20 -40 • As a result, HWRF intensity has improved Day 1 Day 3 Day 5 significantly over the past decade HWRF skill has improved up to 60%! Michael Talk focus: How better use of data, particularly from recon, has helped improve forecasts Michael Talk focus: How better use of data, particularly from recon, has helped improve forecasts History: Using TC Observations
    [Show full text]
  • Introduction to the Principles and Methods of Data Assimilation in the Geosciences
    Introduction to the principles and methods of data assimilation in the geosciences Lecture notes Master M2 MOCIS & WAPE Ecole´ des Ponts ParisTech Revision 0.42 Marc Bocquet CEREA, Ecole´ des Ponts ParisTech 14 January 2014 - 17 February 2014 based on an update of the 2004-2014 lecture notes in French Last revision: 13 March 2021 2 Introduction to data assimilation Contents Synopsis i Textbooks and lecture notes iii Acknowledgements v I Methods of data assimilation 1 1 Statistical interpolation 3 1.1 Introduction . 3 1.1.1 Representation of the physical system . 3 1.1.2 The observational system . 5 1.1.3 Error modelling . 5 1.1.4 The estimation problem . 8 1.2 Statistical interpolation . 9 1.2.1 An Ansatz for the estimator . 9 1.2.2 Optimal estimation: the BLUE analysis . 11 1.2.3 Properties . 11 1.3 Variational equivalence . 13 1.3.1 Equivalence with BLUE . 14 1.3.2 Properties of the variational approach . 14 1.3.3 When the observation operator H is non-linear . 15 1.3.4 Dual formalism . 15 1.4 A simple example . 16 1.4.1 Observation equation and error covariance matrix . 16 1.4.2 Optimal analysis . 16 1.4.3 Posterior error . 17 1.4.4 3D-Var and PSAS . 18 2 Sequential interpolation: The Kalman filter 19 2.1 Stochastic modelling of the system . 20 2.1.1 Analysis step . 21 2.1.2 Forecast step . 21 2.2 Summary, limiting cases and example . 22 2.2.1 No observation . 22 2.2.2 Perfect observations .
    [Show full text]
  • Impact of Radar Reflectivity and Lightning Data Assimilation
    atmosphere Article Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy Stefano Federico 1 , Rosa Claudia Torcasio 1,* , Silvia Puca 2, Gianfranco Vulpiani 2, Albert Comellas Prat 3, Stefano Dietrich 1 and Elenio Avolio 4 1 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, 00133 Rome, Italy; [email protected] (S.F.); [email protected] (S.D.) 2 Dipartimento Protezione Civile Nazionale, 00189 Rome, Italy; [email protected] (S.P.); [email protected] (G.V.) 3 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Strada Pro.Le Lecce-Monteroni, 73100 Lecce, Italy; [email protected] 4 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Zona Industriale ex SIR, 88046 Lamezia Terme, Italy; [email protected] * Correspondence: [email protected] Abstract: Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach Citation: Federico, S.; Torcasio, R.C.; this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able Puca, S.; Vulpiani, G.; Comellas Prat, to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the A.; Dietrich, S.; Avolio, E.
    [Show full text]
  • Ensemble Forecasting and Data Assimilation: Two Problems with the Same Solution?
    Ensemble forecasting and data assimilation: two problems with the same solution? Eugenia Kalnay(1,2), Brian Hunt(2), Edward Ott(2) and Istvan Szunyogh(1,2) (1)Department of Meteorology and (2)Chaos Group University of Maryland, College Park, MD, 20742 1. Introduction Until 1991, operational NWP centers used to run a single computer forecast started from initial conditions given by the analysis, which is the best available estimate of the state of the atmosphere at the initial time. In December 1992, both NCEP and ECMWF started running ensembles of forecasts from slightly perturbed initial conditions (Molteni and Palmer, 1993, Buizza et al, 1998, Buizza, 2005, Toth and Kalnay, 1993, Tracton and Kalnay, 1993, Toth and Kalnay, 1997). Ensemble forecasting provides human forecasters with a range of possible solutions, whose average is generally more accurate than the single deterministic forecast (e.g., Fig. 4), and whose spread gives information about the forecast errors. It also provides a quantitative basis for probabilistic forecasting. Schematic Fig. 1 shows the essential components of an ensemble: a control forecast started from the analysis, two additional forecasts started from two perturbations to the analysis (in this example the same perturbation is added and subtracted from the analysis so that the ensemble mean perturbation is zero), the ensemble average, and the “truth”, or forecast verification, which becomes available later. The first schematic shows an example of a “good ensemble” in which “truth” looks like a member of the ensemble. In this case, the ensemble average is closer to the truth than the control due to nonlinear filtering of errors, and the ensemble spread is related to the forecast error.
    [Show full text]
  • Assimilation of Quikscat/Seawinds Ocean Surface Wind Data Into the JMA Global Data Assimilation System
    Technical Review No. 7 (March 2004) RSMC Tokyo - Typhoon Center Assimilation of QuikSCAT/SeaWinds Ocean Surface Wind Data into the JMA Global Data Assimilation System Yasuaki Ohhashi Numerical Prediction Division, Japan Meteorological Agency 1. Introduction A satellite-borne scatterometer obtains the surface wind vectors over the ocean by measuring the radar signal returned from the sea surface. It provides valuable information such as a typhoon center for numerical weather prediction (NWP) over the ocean, where conventional in situ observations are sparse. Observational data from the European Remote-Sensing Satellite 2 (ERS2) /Active Microwave Instrument (AMI) scatterometer launched by the European Space Agency (ESA) was used operationally at the Japan Meteorological Agency (JMA) from July 1998 to January 2001. A new scatterometer named SeaWinds was launched onboard the QuikSCAT satellite by the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) on 19 June 1999. It was a “quick recovery” mission to fill the gap created by the loss of data from the NASA Scatterometer (NSCAT), when the Japanese Advanced Earth Observation Satellite (ADEOS) lost power in June 1997. The width of swath of QuikSCAT/SeaWinds is 1800 km, which is wider than that of ERS2/AMI by three times, with a spatial resolution of 25 km. Daily coverage is more than 90% of the global ice-free oceans. Figure 1 shows an example of QuikSCAT/SeaWinds observation. The QuikSCAT/SeaWinds observes with higher density than ship or buoy. A cyclonic Fig.1 Wind data around typhoon T0306 (SOUDELOR) observed by QuikSCAT/SeaWinds. Observation time is about 10 UTC 17 June 2003.
    [Show full text]
  • Assimilation Experiments at Ncep Designed to Test Quality Control Procedures and Effective Scale Resolutions for Quikscat / Seawinds Data
    ASSIMILATION EXPERIMENTS AT NCEP DESIGNED TO TEST QUALITY CONTROL PROCEDURES AND EFFECTIVE SCALE RESOLUTIONS FOR QUIKSCAT / SEAWINDS DATA Yu, T.-W.,* and W. H. Gemmill Environmental Modeling Center, National Centers for Environmental Prediction NWS, NOAA, Washington, D. C. 20233 1. INTRODUCTION The second step is the OIQC quality control procedure, which essentially performs a gross error Since January 15, 2002, QuikSCAT wind data have check and a buddy check among like kinds of data, been operationally used in the global data assimilation and is applied to all of global observations within the system (GDAS) at the National Centers for SSI analysis scheme (Wollen,1991). Detailed Environmental Prediction (NCEP). However, there investigation of the OIQC quality control procedure remain at least two outstanding issues on the use of is the subject of forthcoming articles, but suffices it to QuikSCAT wind data in operational numerical weather point out that the OIQC procedure eliminates only prediction. The first issue is related to the rain about 0.1% of the QuikSCAT wind data in a GDAS contamination problem in QuikSCAT wind data, and cycle. Table 1 shows a distribution of TOSSLIST of therefore design of a good quality control procedure QuikSCAT wind data rejected by the OIQC quality for the data becomes critically important. This paper control procedure for 0000 UTC investigates two quality control procedures especially January 19, 2001 of the NCEP global data designed for QuikSCAT winds. The second issue assimilation cycle. These numbers are quite typical, concerns with what scale resolutions of QuikSCAT and they show that the total number of QuikSCAT wind data are the most effective and compatible to the wind data in the analysis cycle is 193,142, of which NCEP atmospheric analysis and data assimilation only 266 observations are rejected by the OIQC system.
    [Show full text]