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This work assimilates multisensor precipitation-sensitive microwave radiance observations into a storm-scale NASA Unified Weather Research and Forecasting (NU-WRF) Model simulation of the West African monsoon. The analysis consists of a full description of the atmospheric states and a realistic cloud and precipitation distribution that is consistent with the observed dynamic and physical features. The analysis shows an improved representation of monsoon precipitation and its interaction with dynamics over West Africa. Most significantly, assimilation of precipitation-affected microwave radiance has a positive impact on the distribution of precipitation intensity and also modulates the propagation of cloud precipitation systems associated with the African easterly jet. Using an ensemble-based assimilation technique that allows state-dependent forecast error covariance among dynamical and microphysical variables, this work shows that the assimilation of precipitation-sensitive microwave radiances over the West African monsoon rainband enables initialization of storms. These storms show the characteristics of continental tropical convection that enhance the connection between tropical waves and organized convection systems.

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.

Global microwave rainfall retrievals from a five-satellite constellation, including the Tropical Rainfall Measuring Mission Microwave Imager, Special Sensor Microwave Imager from the Defense Meteorological Satellite Program F13, F14, and F15, and the Advanced Microwave Scanning Radiometer from the Earth Observing System Aqua, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System using a 1D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses is examined at various temporal and spatial scales. This study demonstrates that the 1D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly mean and 6-h time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1D VCA algorithm, by compensating for errors in the model’s moist time tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall–assimilation run especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicate that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity, are more realistically captured across the front.

This study describes a 1D variational continuous assimilation (VCA) algorithm for assimilating tropical rainfall data using moisture/temperature time-tendency corrections as the control variable to offset model deficiencies. For rainfall assimilation, model errors are of special concern since model-predicted precipitation is based on parameterized moist physics, which can have substantial systematic errors. The authors examine whether a VCA scheme using the forecast model as a weak constraint offers an effective pathway to precipitation assimilation. The particular scheme investigated employs a precipitation observation operator based on a 6-h integration of a column model of moist physics from the Goddard Earth Observing System (GEOS) global data assimilation system (DAS). In earlier studies, a simplified version of this scheme was tested, and improved monthly mean analyses and better short-range forecast skills were obtained. This paper describes the full implementation of the 1DVCA scheme using background and observation error statistics and examines its impact on GEOS analyses and forecasts of prominent tropical weather systems such as hurricanes. Assimilation experiments with and without rainfall data for Hurricanes Bonnie and Floyd show that assimilating 6-h Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Special Sensor Microwave Imager (SSM/I) surface rain accumulations leads to more realistic analyzed storm features and better 5-day storm track prediction and precipitation forecasts. These results demonstrate the importance of addressing model deficiencies in moisture time tendency in order to make effective use of precipitation information in data assimilation.

This article describes a variational framework for assimilating the SSM/I-derived surface rain rate and total precipitable water (TPW) and examines their impact on the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The SSM/I observations consist of tropical rain rates retrieved using the Goddard Profiling Algorithm and tropical TPW estimates produced by Wentz. In a series of assimilation experiments for December 1992, results show that the SSM/I-derived rain rate, despite current uncertainty in its intensity, is better than the model-generated precipitation. Assimilating rainfall data improves cloud distributions and the cloudy-sky radiation, while assimilating TPW data reduces a moisture bias in the lower troposphere to improve the clear-sky radiation. Together, the two data types reduce the monthly mean spatial bias by 46% and the error standard deviation by 26% in the outgoing longwave radiation (OLR) averaged over the Tropics, as compared with the NOAA OLR observation product. The improved cloud distribution, in turn, improves the solar radiation at the surface. There is also evidence that the latent heating change associated with the improved precipitation improves the large-scale circulation in the Tropics. This is inferred from a comparison of the clear-sky brightness temperatures for TIROS Operational Vertical Sounder channel 12 computed from the GEOS analyses with the observed values, suggesting that rainfall assimilation reduces a prevailing moist bias in the upper-tropospheric humidity in the GEOS system through enhanced subsidence between the major convective centers. This work shows that assimilation of satellite-derived precipitation and TPW can reduce state-dependent systematic errors in the OLR, clouds, surface radiation, and the large-scale circulation in the assimilated dataset. The improved analysis also leads to better short-range forecasts, but the impact is modest compared with improvements in the time-averaged signals in the analysis. The study shows that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged ‘‘climate content’’ in the data without comparable improvements in forecast. The full impact of these data types on the analysis cannot be measured solely in terms of forecast skills.