Abstract

Currently, operational weather forecasting systems use observations to optimize the initial state of a forecast without considering possible model deficiencies. For precipitation assimilation, this could be an issue since precipitation observations, unlike conventional data, do not directly provide information on the atmospheric state but are related to the state variables through parameterized moist physics with simplifying assumptions. Precipitation observation operators are comparatively less accurate than those for conventional data or observables in clear-sky regions, which can limit data usage not because of issues with observations, but with the model. The challenge lies in exploring new ways to make effective use of precipitation data in the presence of model errors.

This study continues the investigation of variational algorithms for precipitation assimilation using column model physics as a weak constraint. The strategy is to develop techniques to make online estimation and correction of model errors to improve the precipitation observation operator during the assimilation cycle. Earlier studies have shown that variational continuous assimilation (VCA) of tropical rainfall using moisture tendency correction can improve Goddard Earth Observing System 3 (GEOS-3) global analyses and forecasts. Here results are presented from a 4-yr GEOS-3 reanalysis assimilating Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Special Sensor Microwave Imager (SSM/I) tropical rainfall using the VCA scheme. Comparisons with NCEP operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40) show that the GEOS-3 reanalysis is significantly better at replicating the intensity and variability of tropical precipitation systems ranging from a few days to interannual time scales. As a further refinement of rainfall assimilation using the VCA scheme, a variational algorithm for assimilating TMI latent heating retrievals using semiempirical parameters in the model moist physics as control variables is described and initial test results are presented.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

Abstract

A global analysis that optimally combines observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. In this study it is demonstrated that assimilating precipitation and total precipitable water (TPW) derived from the Tropical Rainfall Measuring Mission Microwave Imager (TMI) can significantly improve the quality of global analysis. It is shown that assimilating the 6-h averaged TMI rainfall and TPW retrievals improves not only the hydrological cycle, but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). Notably, assimilating TMI rain rates improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the Tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Assimilating these data also improves the instantaneous wind and temperature fields in the analysis, leading to better short-range forecasts in the Tropics. Ensemble forecasts initialized with analyses incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day, better 500-hPa geopotential height forecasts up to 5 days, and better 200-hPa divergent winds up to 2 days. These results demonstrate the potential of using high quality spaceborne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications.

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.

As a follow-on to the Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration in the United States, the National Space Development Agency of Japan, and the European Space Agency are considering a satellite mission to measure the global rainfall. The plan envisions an improved TRMM-like satellite and a constellation of eight satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-h intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and two Special Sensor Microwave/Imager (SSM/I) instruments.

Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. It is shown that assimilating the 6-h-averaged TMI and SSM/I surface rain rate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper-tropospheric moisture in the analysis produced by the Goddard Earth Observing System Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System instrument and brightness temperature observations by the Television Infrared Observational Satellite Operational Vertical Sounder instruments.

Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the Tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the Tropics.

These results were obtained using a variational procedure based on a 6-h time integration of a column model of moist physics with prescribed dynamical and other physical tendencies. The procedure estimates moisture tendency corrections at observation locations by minimizing the least square differences between the observed TPW and rain rates and those generated by the column model over a 6-h analysis window. These tendency corrections are then applied during the assimilation cycle to compensate for errors arising from both initial conditions and deficiencies in model physics. Our results point to the importance of addressing deficiencies in model physics in assimilating data types such as precipitation, for which the forward model based on convective parameterizations may have significant systematic errors.

This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of four-dimensional global datasets for climate analysis and weather forecasting applications.

Abstract

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.