Four-Dimensional Variational Assimilation of Precipitation Data (original) (raw)
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Four-Dimensional Variational Assimilation of Total Column Water Vapor in Rainy Areas
Monthly Weather Review, 2002
This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rainrate estimates from the TRMM Microwave Imager.
Hydrological Processes, 2013
The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high-resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real-time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper-air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three-dimensional variational data-assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km 2) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one-dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two-dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases.
Comparison of TMI rainfall estimates and their impact on 4D-Var assimilation
Quarterly Journal of the Royal Meteorological Society, 2002
The objectives of this paper are to perform a comparison between three rainfall-rate estimates from Tropical Rainfall Measuring Mission Microwave Imager (TMI) data and to study their impact in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4D-Var) assimilation system. The three algorithms for rainfall estimation considered are: Precipitation Radar Adjusted TMI Estimation of Rainfall (PATER), Bayesian Algorithm for Microwave-based Precipitation Retrieval (BAMPR-P), and the National Aeronautics and Space Administration operational algorithm 2A12 level 5.
Monthly Weather Review, 2019
The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and...
Geophysical Research Letters, 2010
The impact of assimilating quality-controlled Atmospheric Infrared Sounder (AIRS) temperature retrievals obtained from partially cloudy regions is assessed, with focus on precipitation produced by the GEOS-5 data assimilation and forecasting system, for three tropical cyclones: Nargis (April 27-May 03, 2008) in the Indian Ocean, Wilma (October 15-26, 2005) and Helene (September 12-16, 2006) in the Atlantic. It is found that the precipitation analysis obtained when assimilating AIRS cloudy retrievals (AIRS) can capture regions of heavy precipitation associated with tropical cyclones much better than without AIRS data (CONTRL) or when using AIRS clear-sky radiances (RAD). The precipitation along the storm track shows that the AIRS assimilation produces larger mean values and more intense rain rates than the CONTRL and RAD assimilations. The corresponding precipitation forecasts initialized from AIRS analysis show reasonable prediction skill and better performance than forecasts initialized from CONTRL and RAD analyses up to day-2.
Application of SeaWinds scatterometer and TMI-SSM/I rain rates to hurricane analysis and forecasting
ISPRS Journal of Photogrammetry and Remote Sensing, 2005
Atlas (910), A. Hou (900.3) and 0. Reale (910) Popular s u m m a r y Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager 0 and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.
Atmosphere
This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4)...
Monthly Weather Review, 2015
The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first-and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions. Analysis-forecast experiments are carried out with three types of data-Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT-that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.
TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast
2012
This study evaluates the impact of atmospheric observations from the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observing system on numerical weather prediction of hurricane Ike (2008) using three-dimensional data assimilation system for the Weather Research and Forecast (WRF) model (WRF 3D-Var). The TAMDAR data assimilation capability is added to WRF 3D-Var by incorporating the TAMDAR observation operator and corresponding observation processing procedure. Two 6-h cycling data assimilation and forecast experiments are conducted. Track and intensity forecasts are verified against the best track data from the National Hurricane Center.
Advances in Atmospheric Sciences, 2005
The formulation of the National Centers for Environmental Prediction four-dimensional variational dataassimilation (4D-Var) system is described. Results of applying 4D-Var over a one-week assimilation period, with a full set of physical parametrizations, are presented and compared with those of 3D-Var. The linearization has been performed without simplifications and, therefore, the tangent-linear and adjoint codes are consistent with the nonlinear physical parametrizations. The 4D-Var assimilation is similar in formulation to the 3D-Var analysis, except that observations are used at the appropriate time in 4D-Var. Compared with the 3D-Var runs, the 4D-Var results showed good convergences, smaller analysis increments, and a comparable fit of analyses and short-range forecasts to observations. A consistent improvement with the 4D-Var system is observed in short-range (six-hour) forecasts of all model variables except the specific humidity. The temperature analyses from 4D-Var were found to be better in most of the areas where the analysis errors from 3D-Var were largest, although the globally averaged root-mean-square difference in the 4D-Var temperature analysis was larger due to a very small degradation in some parts of the globe that include data-rich areas. The globally averaged root-mean-square difference in the 4D-Var specific-humidity analysis, compared with that of 3D-Var, was larger and was found to result from slightly increased analysis-error maxima in the 4D-Var results over data-sparse tropical regions. The 3 4 day forecasts from 4D-Var analyses compared more favourably than forecasts from the 3D-Var analyses with the targeted mid-Pacific dropwindsonde observations available from the 1998 North Pacific Experiment. Compared with conventional observations, a consistent improvement in the 1-5 day forecasts of wind and temperature was shown in the tropics and the southern hemisphere.