Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction

2021 ◽  
Author(s):  
Deming Meng ◽  
Yaodeng Chen ◽  
Jun Li ◽  
Hongli Wang ◽  
Yuanbing Wang ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Background Error ◽  
Single Observation ◽  
Numerical Weather ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Analysis Scheme ◽  
The Impact

AbstractThe background error covariance (B) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals Assessing the impact of Argo floats temperature measurements on the numerical weather prediction forecast skill

2019 ◽  
Vol 20 (2) ◽  
pp. 331 ◽  
Author(s):  
GEORGE VARLAS ◽  
PETROS KATSAFADOS ◽  
GERASIMOS KORRES ◽  
ANASTASIOS PAPADOPOULOS
Keyword(s):  
Boundary Conditions ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Forecast Skill ◽  
Temperature Measurements ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Argo Floats ◽  
The Impact

The forecast skill of numerical weather prediction (NWP) models relies, among other factors such as the prediction itself and the assimilation scheme, on the accuracy of the observations utilized in the assimilation systems for the production of initial and boundary conditions. One of the most crucial parameters in weather forecasting is the sea surface temperature (SST). In the majority of NWP models, the initial and lower boundary conditions involve gridded (SST) analyses which consist of data obtained by buoys, ships and satellites. The main aim of this study is to integrate Argo temperature measurements in gridded SST analyses and to assess their impact on the forecast skill of a limited area atmospheric model. Argo floats are “state-of-the-art” oceanographic instruments producing high-quality temperature profiles for the ice-free ocean. In this study, Argo temperatures are incorporated into gridded SST fields without applying any smoothing method in order to directly assess the impact of Argo temperatures on numerical weather prediction. Their impact is assessed under intense weather cyclonic conditions at the Mediterranean Sea by performing two sensitivity simulations either incorporating or not Argo temperatures into gridded SST fields used in the generation of the initial and lower boundary conditions. The results indicate that the inclusion of Argo-measured near-surface temperatures in the lower boundary condition modifies the surface heat fluxes, thus affecting mean sea level pressure and precipitation. In particular, an overall improvement of the precipitation forecast skill up to 3% has been demonstrated. Moreover, the incorporation of Argo temperatures affects the simulated track and intensity of the cyclone over the Balkan Peninsula.

scholarly journals Estimation of GNSS tropospheric products and their meteorological exploitation in Slovakia

2020 ◽  
Vol 50 (1) ◽  
pp. 83-111
Author(s):  
Martin Imrišek ◽  
Mária Derková ◽  
Juraj Janák
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Total Delay ◽  
Numerical Weather ◽  
Zenith Total Delay ◽  
The Impact

This paper discusses the in near–real time processing of Global Navigation Satellite System observations at the Department of Theoretical Geodesy at the Slovak University of Technology in Bratislava. Hourly observations from Central Europe are processed with 30 minutes delay to provide tropospheric products. The time series and maps of tropospheric products over Slovakia are published online. Zenith total delay is the most important tropospheric parameter. Its comparison with zenith total delays from IGS and E–GVAP solutions and the validation of estimated zenith total delay error over year 2018 have been made. Zenith total delays are used to improve initial conditions of numerical weather prediction model by the means of the three–dimensional variational analysis at Slovak Hydrometeorological Institute. The impact of assimilation of different observation types into numerical weather prediction model is discussed. The case study was performed to illustrate the impact of zenith total delay assimilation on the precipitation forecast.

Retrieval of Moisture from Simulated GPS Slant-Path Water Vapor Observations Using 3DVAR with Anisotropic Recursive Filters

2007 ◽  
Vol 135 (4) ◽  
pp. 1506-1521 ◽  
Author(s):  
Haixia Liu ◽  
Ming Xue ◽  
R. James Purser ◽  
David F. Parrish
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Background Error ◽  
Special Configuration ◽  
Background Error Covariance ◽  
Recursive Filters ◽  
Error Covariance ◽  
Slant Path ◽  
The Impact

Abstract Anisotropic recursive filters are implemented within a three-dimensional variational data assimilation (3DVAR) framework to efficiently model the effect of flow-dependent background error covariance. The background error covariance is based on an estimated error field and on the idea of Riishøjgaard. In the anisotropic case, the background error pattern can be stretched or flattened in directions oblique to the alignment of the grid coordinates and is constructed by applying, at each point, six recursive filters along six directions corresponding, in general, to a special configuration of oblique lines of the grid. The recursive filters are much more efficient than corresponding explicit filters used in an earlier study and are therefore more suitable for real-time numerical weather prediction. A set of analysis experiments are conducted at a mesoscale resolution to examine the effectiveness of the 3DVAR system in analyzing simulated global positioning system (GPS) slant-path water vapor observations from ground-based GPS receivers and observations from collocated surface stations. It is shown that the analyses produced with recursive filters are at least as good as those with corresponding explicit filters. In some cases, the recursive filters actually perform better. The impact of flow-dependent background errors modeled using the anisotropic recursive filters is also examined. The use of anisotropic filters improves the analysis, especially in terms of finescale structures. The analysis system is found to be effective in the presence of typical observational errors. The sensitivity of isotropic and anisotropic recursive-filter analyses to the decorrelation scales is also examined systematically.

scholarly journals Impact of model resolution on numerical weather prediction

2019 ◽  
Vol 286 ◽  
pp. 07012
Author(s):  
Z. Sahlaoui ◽  
S. Mordane
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Model Resolution ◽  
Precipitation Field ◽  
Numerical Weather ◽  
North East ◽  
The North ◽  
The Impact

Several model configurations are used in Morocco for numerical weather prediction (NWP). The aim of this work is to verify the impact of resolution on the quality of models forecast, particularly the precipitation field. Three model configurations are tested with 7.5 km, 5 km and 2.5 km resolution. A rainy event over the North-East of Morocco is studied. The impact on models performances is assessed through the comparison of precipitation forecasts with the adjusted quantitative precipitation estimate from weather radar. The results show that the model with 2.5 km resolution gives the best quality precipitation forecast in term of both intensity and localisation.

scholarly journals Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study

2020 ◽  
Author(s):  
Pauline Martinet ◽  
Domenico Cimini ◽  
Frédéric Burnet ◽  
Benjamin Ménétrier ◽  
Yann Michel ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Atmospheric Stability ◽  
Specific Humidity ◽  
Large Temperature ◽  
False Alarms ◽  
Background Error ◽  
Var Analysis ◽  
Numerical Weather ◽  
The Impact

Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the ini- tial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To that end, temperature,humidity and liquid water path (LWP) retrievals have been performed using a one-dimensional variational technique (1D-Var) during a fog dedicated field-experiment performed over winter 2016–2017 in France. In-situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias-correction applied on MWR raw measurements. With the optimal 1D-Var configuration, a root-mean-square error smaller than 1.5 K (resp. 0.8 K) for temperature and 1 g kg−1 (resp. 0.5 g kg−1) for humidity is obtained up to 6 km altitude (resp. within the fog layer up to 250 m). A thin-radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME model as well as vertical and temporal errors observed in the fog lifecycle. During missed fog profiles, 1D-Var increments pull towards lower temperature close to the ground and higher temperature above 100 m altitude, i.e. higher atmospheric stability. The largest analysis increments and background errors are observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. The impact on specific humidity was found neutral to slightly positive. The impact on LWP was found significant with 1D-Var increments within 200 g m−2 and RMSE with respect to MWR statistical regressions decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of 8 MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.

scholarly journals Hybrid Background Error Covariances for a Limited-Area Deterministic Weather Prediction System

2020 ◽  
Vol 35 (3) ◽  
pp. 1051-1066
Author(s):  
Joël Bédard ◽  
Jean-François Caron ◽  
Mark Buehner ◽  
Seung-Jong Baek ◽  
Luc Fillion
Keyword(s):  
Weather Prediction ◽  
Localization Length ◽  
Added Value ◽  
Limited Area ◽  
Prediction System ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact ◽  
Dependent Error

Abstract This study introduces an experimental regional assimilation configuration for a 4D ensemble–variational (4D-EnVar) deterministic weather prediction system. A total of 16 assimilation experiments covering July 2014 are presented to assess both experimental regional climatological background error covariances and updates in the treatment of flow-dependent error covariances. The regional climatological background error covariances are estimated using statistical correlations between variables instead of using balance operators. These error covariance estimates allow the analyses to fit more closely with the assimilated observations than when using the lower-resolution global background error covariances (due to shorter correlation scales), and the ensuing forecasts are significantly improved. The use of ensemble-based background error covariances is also improved by reducing vertical and horizontal localization length scales for the flow-dependent background error covariance component. Also, reducing the number of ensemble members employed in the deterministic analysis (from 256 to 128) reduced computational costs by half without degrading the accuracy of analyses and forecasts. The impact of the relative contributions of the climatological and flow-dependent background error covariance components is also examined. Results show that the experimental regional system benefits from giving a lower (higher) weight to climatological (flow-dependent) error covariances. When compared with the operational assimilation configuration of the continental prediction system, the proposed modifications to the background error covariances improve both surface and upper-air RMSE scores by nearly 1%. Still, the use of a higher-resolution ensemble to estimate flow-dependent background error covariances does not yet provide added value, although it is expected to allow for a better use of dense observations in the future.

scholarly journals On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

scholarly journals The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

2021 ◽  
Vol 13 (3) ◽  
pp. 426
Author(s):  
Zheng Qi Wang ◽  
Roger Randriamampianina
Keyword(s):  
Weather Prediction ◽  
Deterministic System ◽  
Observation Error ◽  
Key Factors ◽  
Background Error ◽  
Current Configuration ◽  
Error Covariance ◽  
Regional Reanalysis ◽  
The Impact ◽  
European Regional

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

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