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.

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 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 Precipitation forecast skill of numerical weather prediction models and radar nowcasts

2005 ◽  
Vol 32 (14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Charles Lin ◽  
Slavko Vasić ◽  
Alamelu Kilambi ◽  
Barry Turner ◽  
Isztar Zawadzki
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

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 Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

2021 ◽  
Author(s):  
Sven Ulbrich ◽  
Christian Welzbacher ◽  
Kobra Khosravianghadikolaei ◽  
Michael Hoff ◽  
Alberto de Lozar ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Additional Measure ◽  
Microphysics Scheme ◽  
Numerical Weather ◽  
Complementary Approach ◽  
Forecast Quality

<p>The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.</p><p>At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) <span>making use of a</span> continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the <span>nowcasting </span>on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.</p><p>The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.</p>

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