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- Author or Editor: Samson H. Cheung x
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Abstract
Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.
Abstract
Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.
Abstract
In the near future, the Global Precipitation Measurement (GPM) mission will provide precipitation observations with unprecedented accuracy and spatial/temporal coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer-resolution precipitation fields. This paper explores a dynamic downscaling method using ensemble data assimilation techniques and cloud-resolving models. A prototype ensemble data assimilation system using the Weather Research and Forecasting Model (WRF) has been developed. A high-resolution regional WRF with multiple nesting grids is used to provide the first-guess and ensemble forecasts. An ensemble assimilation algorithm based on the maximum likelihood ensemble filter (MLEF) is used to perform the analysis. The forward observation operators from NOAA–NCEP’s gridpoint statistical interpolation (GSI) are incorporated for using NOAA–NCEP operational datastream, including conventional data and clear-sky satellite observations. Precipitation observation operators are developed with a combination of the cloud-resolving physics from NASA Goddard cumulus ensemble (GCE) model and the radiance transfer schemes from NASA Satellite Data Simulation Unit (SDSU). The prototype of the system is used as a test bed to optimally combine observations and model information to produce a dynamically downscaled precipitation analysis. A case study on Tropical Storm Erin (2007) is presented to investigate the ability of the prototype of the WRF Ensemble Data Assimilation System (WRF-EDAS) to ingest information from in situ and satellite observations including precipitation-affected radiance. The results show that the analyses and forecasts produced by the WRF-EDAS system are comparable to or better than those obtained with the WRF-GSI analysis scheme using the same set of observations. An experiment was also performed to examine how the analyses and short-term forecasts of microphysical variables and dynamical fields are influenced by the assimilation of precipitation-affected radiances. The results highlight critical issues to be addressed in the next stage of development such as model-predicted hydrometeor control variables and associated background error covariance, bias estimation, and correction in radiance space, as well as the observation error statistics. While further work is needed to optimize the performance of WRF-EDAS, this study establishes the viability of developing a cloud-scale ensemble data assimilation system that has the potential to provide a useful vehicle for downscaling satellite precipitation information to finer scales suitable for hydrological applications.
Abstract
In the near future, the Global Precipitation Measurement (GPM) mission will provide precipitation observations with unprecedented accuracy and spatial/temporal coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer-resolution precipitation fields. This paper explores a dynamic downscaling method using ensemble data assimilation techniques and cloud-resolving models. A prototype ensemble data assimilation system using the Weather Research and Forecasting Model (WRF) has been developed. A high-resolution regional WRF with multiple nesting grids is used to provide the first-guess and ensemble forecasts. An ensemble assimilation algorithm based on the maximum likelihood ensemble filter (MLEF) is used to perform the analysis. The forward observation operators from NOAA–NCEP’s gridpoint statistical interpolation (GSI) are incorporated for using NOAA–NCEP operational datastream, including conventional data and clear-sky satellite observations. Precipitation observation operators are developed with a combination of the cloud-resolving physics from NASA Goddard cumulus ensemble (GCE) model and the radiance transfer schemes from NASA Satellite Data Simulation Unit (SDSU). The prototype of the system is used as a test bed to optimally combine observations and model information to produce a dynamically downscaled precipitation analysis. A case study on Tropical Storm Erin (2007) is presented to investigate the ability of the prototype of the WRF Ensemble Data Assimilation System (WRF-EDAS) to ingest information from in situ and satellite observations including precipitation-affected radiance. The results show that the analyses and forecasts produced by the WRF-EDAS system are comparable to or better than those obtained with the WRF-GSI analysis scheme using the same set of observations. An experiment was also performed to examine how the analyses and short-term forecasts of microphysical variables and dynamical fields are influenced by the assimilation of precipitation-affected radiances. The results highlight critical issues to be addressed in the next stage of development such as model-predicted hydrometeor control variables and associated background error covariance, bias estimation, and correction in radiance space, as well as the observation error statistics. While further work is needed to optimize the performance of WRF-EDAS, this study establishes the viability of developing a cloud-scale ensemble data assimilation system that has the potential to provide a useful vehicle for downscaling satellite precipitation information to finer scales suitable for hydrological applications.
