scholarly journals Parameter Sensitivity of the WRF–LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments

2020 ◽  
Vol 35 (4) ◽  
pp. 1345-1362 ◽  
Author(s):  
Paula Maldonado ◽  
Juan Ruiz ◽  
Celeste Saulo
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
System Simulation ◽  
Weather Prediction ◽  
Doppler Velocity ◽  
Convective System ◽  
Model Errors ◽  
Spatiotemporal Resolution ◽  
Simulation Experiments ◽  
The Impact

AbstractSpecification of suitable initial conditions to accurately forecast high-impact weather events associated with intense thunderstorms still poses a significant challenge for convective-scale forecasting. Radar data assimilation has been showing encouraging results to produce an accurate estimate of the state of the atmosphere at the mesoscale, as it combines high-spatiotemporal-resolution observations with convection-permitting numerical weather prediction models. However, many open questions remain regarding the configuration of state-of-the-art data assimilation systems at the mesoscale and their potential impact upon short-range weather forecasts. In this work, several observing system simulation experiments of a mesoscale convective system were performed to assess the sensitivity of the local ensemble transform Kalman filter to both relaxation-to-prior spread (RTPS) inflation and horizontal localization of the error covariance matrix. Realistic large-scale forcing and model errors have been taken into account in the simulation of reflectivity and Doppler velocity observations. Overall, the most accurate analyses in terms of RMSE were produced with a relatively small horizontal localization cutoff radius (~3.6–7.3 km) and large RTPS inflation parameter (~0.9–0.95). Additionally, the impact of horizontal localization on short-range ensemble forecast was larger compared to inflation, almost doubling the lead times up to which the effect of using a more accurate state to initialize the forecast persisted.

Improved impacts in observing system simulation experiments of radio occultation observations as a result of model and data assimilation changes

2020 ◽  
Author(s):  
L. CUCURULL ◽  
S. P. F. CASEY
Keyword(s):  
Data Assimilation ◽  
Radio Occultation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
System Simulation ◽  
Weather Prediction ◽  
Wind Fields ◽  
Simulation Experiments ◽  
The Impact ◽  
Forward Operator

AbstractAs global data assimilation systems continue to evolve, Observing System Simulation Experiments (OSSEs) need to be updated to accurately quantify the impact of proposed observing technologies in weather forecasting. Earlier OSSEs with radio occultation (RO) observations have been updated and the impact of the originally proposed Constellation Observing Satellites for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) mission, with a high-inclination and low-inclination component, has been investigated by using the operational data assimilation system at NOAA and a 1-dimensional bending angle RO forward operator. It is found that the impact of the low-inclination component of the originally planned COSMIC-2 mission (now officially named COSMIC-2) has significantly increased as compared to earlier studies, and significant positive impact is now found globally in terms of mass and wind fields. These are encouraging results as COSMIC-2 was successfully launched in June 2019 and data have been recently released to operational weather centers. Earlier findings remain valid indicating that globally distributed RO observations are more important to improve weather prediction globally than a denser sampling of the tropical latitudes. Overall, the benefits reported here from assimilating RO soundings are much more significant than the impacts found in previous OSSEs. This is largely attributed to changes in the data assimilation and forecast system and less to the more advanced 1-dimensional forward operator chosen for the assimilation of RO observations.

Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction

2015 ◽  
Vol 32 (9) ◽  
pp. 1593-1613 ◽  
Author(s):  
Robert Atlas ◽  
Ross N. Hoffman ◽  
Zaizhong Ma ◽  
G. David Emmitt ◽  
Sidney A. Wood ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
System Simulation ◽  
Weather Prediction ◽  
Simulation Experiments ◽  
Wind Lidar ◽  
Potential Impact ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

AbstractThe potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

scholarly journals The Impact of Covariance Localization for Radar Data on EnKF Analyses of a Developing MCS: Observing System Simulation Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 3691-3709 ◽  
Author(s):  
Ryan A. Sobash ◽  
David J. Stensrud
Keyword(s):  
System Simulation ◽  
Three Dimensional ◽  
Radar Data ◽  
Convective System ◽  
State Variables ◽  
Simulation Experiments ◽  
Length Scales ◽  
Convective Scale ◽  
The Impact ◽  
Prior State

Abstract Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

scholarly journals The Impact of Airborne Doppler Aerosol Wind (DAWN) Lidar Wind Profiles on Numerical Simulations of Tropical Convective Systems during the NASA Convective Processes Experiment (CPEX)

2020 ◽  
Vol 37 (4) ◽  
pp. 705-722 ◽  
Author(s):  
Zhiqiang Cui ◽  
Zhaoxia Pu ◽  
G. David Emmitt ◽  
Steven Greco
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Tropical Storm ◽  
Convective System ◽  
Spatiotemporal Resolution ◽  
Convective Systems ◽  
Convective Processes ◽  
Wind Profiles ◽  
The Impact

AbstractHigh-spatiotemporal-resolution airborne Doppler Aerosol Wind (DAWN) lidar profiles over the Caribbean Sea and Gulf of Mexico region were collected during the NASA Convective Processes Experiment (CPEX) field campaign from 27 May to 24 June 2017. This study examines the impact of assimilating these wind profiles on the numerical simulation of moist convective systems using an Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW). A mesoscale convective system and a tropical storm (Cindy) that occurred on 16 June 2017 in a strong shear environment and on 21 June 2017 in a weak shear environment, respectively, are selected as case studies. The DAWN wind profiles are assimilated with the NCEP Gridpoint Statistical Interpolation analysis system using a three-dimensional variational (3DVar) and a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation systems to provide the initial conditions for a short-range forecast. Results show that the assimilation of DAWN wind profiles has significant positive impacts on convective simulations with the two assimilation approaches. The assimilation of DAWN wind profiles creates notable adjustments in the analysis of the divergence field for WRF simulations with a good agreement of wind forecasts with radiosonde observations. The quantitative precipitation forecasting is also improved. In general, the 3DEnVar data assimilation method is deemed more promising for DAWN data assimilation. There are cases with Tropical Storm Cindy in which DAWN data have slight to neutral impact on rainfall forecasts with 3DVAR, implying complicated interactions between errors of retrieved wind data and background error covariance in the lower and upper troposphere.

scholarly journals The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

2013 ◽  
Vol 141 (1) ◽  
pp. 93-111
Author(s):  
Luiz F. Sapucci ◽  
Dirceu L. Herdies ◽  
Renata W. B. Mendonça
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Southern Hemisphere ◽  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Range Forecasts

Abstract Water vapor plays a crucial role in atmospheric processes and its distribution is associated with cloud-cover fraction and rainfall. The inclusion of integrated water vapor (IWV) estimates in numerical weather prediction improves the vertical structure of the humidity analysis and consequently contributes to obtaining a more realistic atmospheric state. Currently, satellite remote sensing is the most important source of humidity measurements in the Southern Hemisphere, providing information with good horizontal resolution and global coverage. In this study, the inclusion of IWV retrieved from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU) and Special Sensor Microwave Imager (SSM/I) were investigated as additional information in the Physical-space Statistical Analysis System (PSAS), which is the operational data assimilation system at the Center for Weather Forecasting and Climate Studies of the Brazilian National Institute for Space Research (CPTEC/INPE). Experiments were carried out with and without the assimilation of IWV values from both sensors. Results show that, in general, the IWV assimilation reduces the error in short-range forecasts of humidity profile, particularly over tropical regions. In these experiments, an analysis of the impact of the inclusion of IWV values from SSM/I and AIRS/AMSU sensors was done. Results indicated that the impact of the SSM/I values is significant over high-latitude oceanic regions in the Southern Hemisphere, while the impact of AIRS/AMSU values is more significant over continental regions where surface measurements are scarce, such as the Amazonian region. In that area the assimilation of IWV values from the AIRS/AMSU sensor shows a tendency to reduce the overestimate of the precipitation in short-range forecasts.

scholarly journals An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW)

2009 ◽  
Vol 137 (5) ◽  
pp. 1687-1704 ◽  
Author(s):  
Chengsi Liu ◽  
Qingnong Xiao ◽  
Bin Wang
Keyword(s):  
Data Assimilation ◽  
System Simulation ◽  
Variational Data Assimilation ◽  
Water Model ◽  
Sequential Algorithm ◽  
Forecast Errors ◽  
Sampling Errors ◽  
Simulation Experiments ◽  
Real Dimension ◽  
The Impact

Abstract An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the variational algorithm but avoids the need for a tangent linear model and its adjoint so that it can be easily incorporated into variational assimilation systems. The current study explores techniques for En4DVAR application in real-dimension data assimilation. The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVAR upon its analysis. With the Advanced Research Weather Research and Forecasting (ARW-WRF) model, Observing System Simulation Experiments (OSSEs) are designed and their performance in real-dimension data assimilation is examined. It is found that the designed En4DVAR localization techniques can effectively alleviate the impacts of sampling errors upon analysis. Most forecast errors and biases in ARW are reduced by En4DVAR compared to those in a control experiment. En3DVAR cycling experiments are used to compare the ensemble-based sequential algorithm with the ensemble-based retrospective algorithm. These experiments indicate that the ensemble-based retrospective assimilation, En4DVAR, produces an overall better analysis than the ensemble-based sequential algorithm, En3DVAR, cycling approach.

scholarly journals Assimilation of Circumpolar Wind Vectors Derived from Highly Elliptical Orbit Imagery: Impact Assessment Based on Observing System Simulation Experiments

2013 ◽  
Vol 52 (8) ◽  
pp. 1891-1908 ◽  
Author(s):  
L. Garand ◽  
J. Feng ◽  
S. Heilliette ◽  
Y. Rochon ◽  
A. P. Trishchenko
Keyword(s):  
System Simulation ◽  
Weather Prediction ◽  
Elliptical Orbit ◽  
Added Value ◽  
The Arctic ◽  
Polar Regions ◽  
Simulation Experiments ◽  
The Real ◽  
Forecast Validation ◽  
The Impact

AbstractThere is a well-recognized spatiotemporal meteorological observation gap at latitudes higher than 55°, especially in the region 55°–70°. A possible solution to address this issue is a constellation of four satellites in a highly elliptical orbit (HEO), that is, two satellites for each polar region. An important satellite product to support weather prediction is atmospheric motion wind vectors (AMVs). This study uses observing system simulation experiments (OSSEs) to evaluate the benefit to forecasts resulting from the assimilation of HEO AMVs covering one or both polar regions. The OSSE employs the operational global data assimilation system of the Canadian Meteorological Center. HEO AMVs are assimilated north of 50°N and south of 50°S. From 2-month assimilation cycles, the study examines the following three issues: 1) the impact of AMV assimilation in the real system, and how this compares to the impact seen in the simulated system, 2) the added value of HEO AMVs in the Arctic on top of what is currently available, and 3) the relative impact of HEO AMVs in the Arctic and Antarctic in comparison with no AMVs. Although the simulated impact of currently available AMVs is somewhat higher than the real impact, a firm conclusion is that the added value of Arctic HEO AMVs is substantial, improving predictability at days 3–5 by a few hours in terms of 500-hPa geopotential height. The impact of HEO AMVs is relatively stronger in the Southern Hemisphere. Forecast validation of atmospheric profiles against the simulated “true” state and against analyses generated within the assimilation cycles yields very similar results beyond 48 h.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

2015 ◽  
Vol 32 (3) ◽  
pp. 478-495 ◽  
Author(s):  
Zaizhong Ma ◽  
Lars Peter Riishøjgaard ◽  
Michiko Masutani ◽  
John S. Woollen ◽  
George D. Emmitt
Keyword(s):  
System Simulation ◽  
Weather Prediction ◽  
National Research Council ◽  
Wind Data ◽  
Lidar Data ◽  
Data Types ◽  
Simulation Experiments ◽  
Wind Lidar ◽  
The Impact ◽  
Assimilation System

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

Estimation of TAMDAR Observational Error and Assimilation Experiments

2012 ◽  
Vol 27 (4) ◽  
pp. 856-877 ◽  
Author(s):  
Feng Gao ◽  
Xiaoyan Zhang ◽  
Neil A. Jacobs ◽  
Xiang-Yu Huang ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Observational Error ◽  
Error Statistics ◽  
Spatiotemporal Resolution ◽  
Wind Speeds ◽  
Observational Errors ◽  
The Impact

Abstract Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

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