Assimilation Of Satellite Radiances Research Articles

Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.

Improved regional forecasting of an extreme Arctic cyclone in August 2016 with WRF MRI‐4DVAR

AbstractCycling data assimilation and forecast experiments in August 2016 together with a case study of an intense Arctic cyclone (AC16) are performed. Initial conditions from newly developed Multi‐Resolution Incremental Four‐Dimensional Variational (MRI‐4DVAR) and Three‐Dimensional Variational (3DVAR) data assimilation along with forecasts from the polar version of the Weather Research and Forecasting (Polar WRF) model, mimicking operational configurations, are applied. The tasks are to evaluate MRI‐4DVAR performance during a 20‐day cycling run, to investigate the impacts of initial conditions on the forecast skill of AC16, and to identify the factors impacting AC16's predictability. The results from the 20‐day cycling period demonstrate the robustness and reliability of MRI‐4DVAR for data assimilation and subsequent forecast skill. Multiple processes, including mergers of Arctic cyclones, mergers of vortices, vertical coupling between low‐level and upper‐level circulations, baroclinic processes and jet stream forcing, contributed to the generation and development of AC16. Compared to the initial conditions from 4DVAR, 3DVAR produced amplified polar vortices, stronger baroclinic instability, intensified upper‐level jet streams and a stronger low‐level frontal zone, causing the overdevelopment of AC16 in 3DVAR‐based forecasts. For MRI‐4DVAR, the accurate prediction of AC16 5–7 days ahead is likely due primarily to the more accurate representation of upper‐level atmospheric fields, that was facilitated by better satellite radiance assimilation with MRI‐4DVAR that also produced a balanced initial model state. It is concluded that the high‐resolution Polar WRF which is optimized for Arctic conditions combined with 4DVAR facilitated the improved prediction of AC16 compared to the Global Forecast System (GFS) operational deterministic global forecast.

Physics constraint Deep Learning based radiative transfer model.

Deep Learning (DL) open sources libraries such as TensorFlow, Keras, and PyTorch have been widely and successfully applied for many applications in a forward model. We have developed the DL radiative transfer model over Oceans under a clear-sky condition. However, the derived physical model from the DL forward model has difficulties in predicting physical properties such as the Jacobian, because multiple solutions can fit the forward model results during the deep learning training process. The Jacobian model in a radiative transfer can calculate radiance sensitivities on geophysical parameters, which are required by satellite radiance assimilation in support of weather forecasts and for retrieving environmental data records. In this study, we introduce a physics constraint into our deep learning training for deriving the forward model that retains right physics. With this physics constraint, the radiance sensitivities are well captured by this new DL radiative transfer.

Evaluation of CAMEL over the Taklimakan Desert Using Field Observations

Infrared (IR) land surface emissivity (LSE) plays an important role in numerical weather prediction (NWP) models through the satellite radiance assimilation. However, due to the large uncertainties in LSE over the desert, many land-surface sensitive channels of satellite IR sensors are not assimilated. This calls for further assessments of the quality of satellite-retrieved LSE in these desert regions. A set of LSE observations were made from field experiments conducted on 16–18 October 2013 along a south/north desert road in the Taklimakan Desert (TD), China. The observed LSEs (EOBS) are thus used in this study as the reference values to evaluate the quality of Combined ASTER MODIS Emissivity over Land (CAMEL) data. Analysis of these data shows four main results. First, the CAMEL datasets appear to sufficiently capture the spatial variations in LSE from the oasis to the hinterland of the TD (this is especially the case in the quartz reststrahlen band). From site 1 at the southern edge of the Taklimakan Desert to site 10 at the northern edge, the measured LSE and the corresponding CAMEL observation in the quartz reststrahlen band first decrease and reach their minimum around sites 4–6 in the hinterland of the Taklimakan Desert. Then, the LSE increases gradually and finally reaches its maximum at site 10, which has a clay ground surface, showing that the LSE is higher at the edges of the desert and lower in the center. Second, the CAMEL values at 11.3 μm have a zonal distribution characterized by a northeast–southwest strike, though such an artifact might have been introduced by ASTER LSE data during the merging process that created the CAMEL dataset. Third, the unrealistic variation of the original EOBS can be filtered out with useful signals, as identified by the first six principal components of the PCA conducted on the laboratory-measured hyperspectral emissivity spectra (ELAB). Fourth, the CAMEL results correlate well with the measured LSE at the 10 observation sites, with the observed LSE being slightly smaller than the CAMEL values in general.

Estimation of AMSU-A Radiance Observation Impacts in an LETKF-Based Atmospheric Global Data Assimilation System: Comparison with EFSO and Observing System Experiments

Abstract This work assesses the contribution of assimilating AMSU-A satellite-based radiance measurements to a global data assimilation system based on an atmospheric general circulation model and the local ensemble transform Kalman filter (LETKF). The radiance measurements were from three channels that are sensitive to the upper troposphere and lower stratosphere. The contribution of these measurements, or AMSU-A observation impact, was estimated both through ensemble-based forecast sensitivity to observations (EFSO) and observing system experiments (OSEs). Two streams of data-denial experiments for the AMSU-A observations were performed for about one month during winter in each hemisphere. The OSEs quantified the accumulated observation impact by cycling (repeating) data denials: including AMSU-A observations reduced the total observation impact for all observations of each data assimilation cycle. In contrast, EFSO estimated AMSU-A to increase the total observation impact. The opposing effects were attributed to the accumulated observation impact in the OSEs; the accumulation could stabilize the data assimilation cycles. In both experiments, the accumulated observation impact of AMSU-A was strongest in the upper troposphere, particularly in the austral midlatitudes where westerly jets exist and observations of other types are sparse. EFSO also assessed AMSU-A to have the most beneficial observation impact in similar locations. The AMSU-A observation impact tended to accumulate just downstream of where EFSO estimated the beneficial observation impact signals. The accumulated AMSU-A observation impact was tied to dynamic processes in the upper-tropospheric and general stratospheric circulation. Therefore, EFSO helps estimate the beneficial distributions of AMSU-A accumulated observation impact by considering their dynamical propagation. Significance Statement The Advanced Microwave Sounding Unit-A (AMSU-A) satellite radiance assimilation technique was successfully integrated into the Atmospheric General Circulation Model for the Earth Simulator (AFES)–LETKF data assimilation system. We conducted OSEs and used EFSO to assess the AMSU-A observation impact. The two estimation methods identified opposite observation impacts due to the cycling (repeating) OSEs of the AMSU-A observations. We interpreted the causes of the opposite estimations. However, even for the cycling OSEs, EFSO appeared to help estimate distributions of the accumulated observation impact. It is important to consider the dynamical propagation of accumulated observation impact in general circulation.

Satellite radiance assimilation using the 3D-var technique for the heavy rainfall over the Indian region

The present study evaluates the impact of satellite radiance assimilation on the predictability of the WRF model. The 3D-VAR system of the WRF model was used to assimilate satellite radiance data at the initial condition. Four thunderstorm cases were considered, which occurred over Kolkata, India. A set of two numerical experiments, namely CNTL (no-assimilation) and SAT (satellite radiance assimilation), were carried out, and the WRF model was integrated for 24 hours. The assimilation of satellite radiance had improved the wind and moisture fields at the initial condition, which led to evolve the conducive atmosphere further. Results showed that even though thunderstorms are a small-scale system, assimilation of satellite radiance had shown significant improvement in the simulation of the location, evolution, and intensity of thunderstorms. Temporal evolution of surface parameters (such as 2-m temperature and rainfall) was closer to observation (AWS) in SAT experiment. Further, the SAT experiments were able to produce stronger updrafts/downdrafts and increased moisture content deep into the atmosphere. These findings suggest that satellite radiance data assimilation significantly impacts initial conditions, providing a conducive atmosphere.

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

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) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

Multi-sensor analyses of the skin temperature for the assimilation of satellite radiances in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS, cycle 47R1)

Abstract. The assimilation of clear-sky radiance in the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric analysis relies on the clear-sky radiances observation operator. Some of these radiances have frequencies that make them sensitive to both the surface and atmosphere. Because the atmospheric and surface analyses are currently not strongly coupled, a specific treatment of the surface is required. The observation operator specifically expects a skin temperature value at the observation location and time as well as the profiles of the atmospheric variables along the viewing path. This skin temperature is added to the control variable and optimised simultaneously with all of the atmospheric variables to produce optimal simulated radiances. We present two approaches to add the skin temperature to the control variable. In the current TOVS Control Variable (TOVSCV) approach, a series of skin temperature values per observation location is added to the control variable. Effectively, in the optimisation process, the skin temperature acts as a sink variable in observation space and is uncoupled from the skin temperature at other locations. In the novel SKin Temperature in the Extended Control Vector (SKTECV) approach, two-dimensional skin temperature fields are added to the control variable. All clear-sky radiances then participate in the optimisation of these two-dimensional fields, and the analysis produces temporally and spatially consistent skin temperature fields. We compare the two approaches over two seasons of 3 months each. Overall, there is a neutral impact of the new approach on the analysis and forecast. Moreover, there is some evidence that the contribution of the subsurface layers should be represented in the new approach for the skin temperature associated with the microwave instruments.

Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13 μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39 μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

Evaluation of the assimilation of conventional and satellite-based observations in simulating heavy rainfall event using WRFDA over the North-West Himalayan region

Present work elucidates the impact of 3DVAR data assimilation technique for the simulation of one of the heavy rainfall events reported over Kotdwara region in the North-West Himalayan (NWH) region on 4th August 2017. We have examined the impact of conventional and satellite-based radiance datasets on the simulated results with and without assimilating the observations into the Weather Research and Forecasting (WRF) model. Three experiments have been designed with 3 nested domains of variable resolutions, one without assimilation (referred as control experiment) and other two experiments after assimilating conventional and satellite radiances observations (refer as DA-OBS and DA-SAT respectively). In the present study, assimilation of surface, upper air and the satellite-based radiance observations has been carried out for the outermost domain with horizontal resolution of 9 km. Statistical analysis suggests that the correlation coefficient is high (0.55) and root mean square error (RMSE) is low (17.12) for DA-SAT experiment as compared to other two experiments. Substantial improvement in the location, pattern and intensity of extreme rainfall event is noted after assimilation of both conventional and satellite observations with respect to the observed rainfall data. However, it is noted that the assimilation of satellite radiances has greater impact in simulating better intensity of the heavy rainfall event as compared to the assimilation of conventional observations. Plausible reason behind this could be the non-availability of the conventional observations close to the extreme rainfall event affected region.

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

AbstractCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.

SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

AbstractSINGV‐DA is a convective‐scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV‐DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV‐DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system.

Fast 3-D Inhomogeneous Radiative Transfer Model Using a Planar-Stratified Forward Algorithm and Horizontal Perturbation Series

A horizontally inhomogeneous unified microwave radiative transfer (HI-UMRT) model is presented to study the 3-D effects of the horizontal inhomogeneous clouds on the computed microwave radiances and facilitate satellite radiance assimilation over the horizontally inhomogeneous all-weather conditions. HI-UMRT provides a coupled two-Stokes parameter numerical radiance solution of the 3-D radiative transfer (RT) equation by embedding the existing 1-D UMRT algorithm into an iterative perturbation scheme. The horizontal derivatives in the radiances of the lower perturbation order are treated as the source functions of the azimuthal harmonic perturbation RT equations that are readily solved using the planar-stratified 1-D UMRT algorithm. The horizontal radiance derivative is estimated by central differencing. A perturbation source-function analysis shows that the increase in computing time for the 3-D HI-UMRT model relative to the 1-D UMRT model is moderate, since: 1) the computationally efficient UMRT engine is applied only to the perturbation equations with nontrivial solutions and 2) the layer parameters for the 1-D solution are reused for all higher perturbation orders. Numerical simulations using HI-UMRT based on 3-D cloud profiles simulated by the Weather Research and Forecasting numerical weather model illustrate the convergence of the iterative perturbation series. An intercomparison of the top-of-atmosphere brightness temperature images for HI-UMRT versus the change planar-stratifiedslant path UMRT model illustrates the considerable impact of cloud horizontal inhomogeneities on the computed upwelling microwave radiances.

A Two-Season Impact Study of Radiative Forced Tropospheric Response to Stratospheric Initial Conditions Inferred From Satellite Radiance Assimilation

This study investigated the impacts of stratospheric temperatures and their variations on tropospheric short-term weather forecasting using the Advanced Research Weather Research and Forecasting (WRF-ARW) system with real satellite data assimilation. Satellite-borne microwave stratospheric temperature measurements up to 1 mb, from the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS), and the Special Sensor microwave Imager/Sounder (SSMI/S), were assimilated into the WRF model over the continental U.S. during winter and summer 2015 using the community Gridpoint Statistical Interpolation (GSI) system. Adjusted stratospheric temperature related to upper stratospheric ozone absorption of short-wave (SW) radiation further lead to vibration in downward SW radiation in winter predictions and overall reduced with a maximum of 5.5% reduction of downward SW radiation in summer predictions. Stratospheric signals in winter need 48- to 72-h to propagate to the lower troposphere while near-instant tropospheric response to the stratospheric initial conditions are observed in summer predictions. A schematic plot illustrated the physical processes of the coupled stratosphere and troposphere related to radiative processes. Our results suggest that the inclusion of the entire stratosphere and better representation of the upper stratosphere are important in regional NWP systems in short-term forecasts.

Impact of Slant-Path Radiative Transfer in the Simulation and Assimilation of Satellite Radiances in Environment Canada’s Weather Forecast System

Abstract Slant satellite-viewing geometry is investigated for the simulation and assimilation of radiances in Environment Canada’s weather forecast system. The standard approach is to extract from a short-term forecast (trial field) a one-dimensional vertical profile, located at the ground footprint of the observation location, to compute the model equivalent of the observation. Since in general, the lines of sight are not vertical, the observation operator can be improved by interpolating the trial field to the slant path. The interpolation relies on the estimation of horizontal gradients of atmospheric variables, extracted at each altitude layer, and representative of a neighborhood of approximately 100 km. Using these slant profiles of the trial fields yields significant improvements in the simulation and assimilation of radiance observations. The impact of this modification in assimilation and forecasts is evaluated over two periods of two months. Both short-term and long-term effects are found, obtained not only from the more accurate interpolation of the trial fields, but also from a revision of the observation error suggested by the lower observation minus trial field difference statistics. The error standard deviation of the geopotential height shows, in the upper troposphere and lower stratosphere, in 12–48-h forecasts, up to 10% reduction in the zonal value at higher latitudes, and 1%–2% reduction in the global value. Given these improved statistics, a revision of the radiance error statistics was explored. Applying these revised statistics suggests that the impact of a slant operator extends into longer range.

Improvements to Cloud-Top Brightness Temperatures Computed from the CRTM at 3.9 μm

ABSTRACTIn preparation for all-sky satellite radiance assimilation, the Community Radiative Transfer Model (CRTM), version 2.1.3, was used to produce Geostationary Operational Environmental Satellite-12/13 (GOES-12/13) imagery near 3.9 μm. For the current study, model output simulated from different models, microphysics, and weather events was used by the CRTM to generate imagery over and near the United States. A direct comparison of observed and CRTM GOES-12/13 imagery near 3.9 μm revealed that CRTM brightness temperatures of solid-water cold cloud tops were approximately 30 K less than observed values. Two CRTM errors were identified and resolved: 1) a coding error that was found by the CRTM team and 2) incorrect optical properties of ice, resulting in improved values of brightness temperatures. Further, changes in microphysics also contributed to improvements, save for one case. The coding error solution appeared in the publicly released CRTM, version 2.3.0, on 27 November 2017, while the inclusion of the optical property solution is undetermined. Since the CRTM is the radiative transfer model within the operational data assimilation system at the National Centers for Environmental Prediction (NCEP), improvements to both the CRTM and model microphysics will be beneficial for future all-sky radiance assimilation activities.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level (surface-sensitive) channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets. Here, we used an improved land use and leaf area index (LAI) dataset in the WRF-3DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels (e.g., channel 3), the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.

Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system

This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.