scholarly journals Progress in Forecast Skill at Three Leading Global Operational NWP Centers during 2015–17 as Seen in Summary Assessment Metrics (SAMs)

2018 ◽  
Vol 33 (6) ◽  
pp. 1661-1679 ◽  
Author(s):  
Ross N. Hoffman ◽  
V. Krishna Kumar ◽  
Sid-Ahmed Boukabara ◽  
Kayo Ide ◽  
Fanglin Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Short Term ◽  
Absolute Value ◽  
Term Forecast ◽  
Assessment Metrics ◽  
Primary Assessment ◽  
Better Than

Abstract The summary assessment metric (SAM) method is applied to an array of primary assessment metrics (PAMs) for the deterministic forecasts of three leading numerical weather prediction (NWP) centers for the years 2015–17. The PAMs include anomaly correlation, RMSE, and absolute mean error (i.e., the absolute value of bias) for different forecast times, vertical levels, geographic domains, and variables. SAMs indicate that in terms of forecast skill ECMWF is better than NCEP, which is better than but approximately the same as UKMO. The use of SAMs allows a number of interesting features of the evolution of forecast skill to be observed. All three centers improve over the 3-yr period. NCEP short-term forecast skill substantially increases during the period. Quantitatively, the effect of the 11 May 2016 NCEP upgrade to the four-dimensional ensemble variational data assimilation (4DEnVar) system is a 7.37% increase in the probability of improved skill relative to a randomly chosen forecast metric from 2015 to 2017. This is the largest SAM impact during the study period. However, the observed impacts are within the context of slowly improving forecast skill for operational global NWP as compared to earlier years. Clearly, the systems lagging ECMWF can improve, and there is evidence from SAMs in addition to the 4DEnVar example that improvements in forecast and data assimilation systems are still leading to forecast skill improvements.

A Hybrid Differential-Ensemble Linear Forecast Model for 4D-Var

2021 ◽  
Vol 149 (1) ◽  
pp. 3-19
Author(s):  
T. J. Payne
Keyword(s):  
Data Assimilation ◽  
Linear Model ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Forecast Model ◽  
Regression Technique ◽  
Time Step ◽  
Dynamical Core ◽  
Numerical Weather ◽  
Better Than

AbstractA key component of the 4D-Var data assimilation method used widely for numerical weather prediction is the linear forecast model, which is approximately tangent linear to the forecast model. Traditionally this has been based on differentiating the forecast model, though recently some authors have experimented with an ensemble regression technique, the localized ensemble tangent linear model (LETLM). We propose a hybrid of the two, in which a simplified conventional tangent-linear model (e.g., just the dynamical core) is used together with an LETLM-like adjustment every time step to account for the remaining processes (in this example, the parameterized physics). This is much cheaper than the LETLM, and in tests using the Met Office’s linear model performs considerably better than either a pure LETLM (with a very large ensemble) or the existing linear model.

scholarly journals Assimilation of MWHS-2/FY-3C 183 GHz Channels Using a Dynamic Emissivity Retrieval and Its Impacts on Precipitation Forecasts: A Southwest Vortex Case

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Keyi Chen ◽  
Jiao Fan ◽  
Zhipeng Xian
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Numerical Weather ◽  
Microwave Humidity Sounder ◽  
Southwest Vortex ◽  
Polar Orbiting Satellite ◽  
Better Than

The dynamic emissivity retrieved from window channels of the microwave humidity sounder II (MWHS-2) onboard the China Meteorological Administration’s FengYun (FY)-3C polar orbiting satellite can provide more realistic emissivity over lands and potentially improve the numerical weather prediction (NWP) forecasts. However, whether the assimilation with the dynamic emissivity works for the precipitation forecasts over the complex geography is less investigated. In this paper, a typical precipitating case generated by the Southwest Vortex is selected and the Weather Research and Forecasting data assimilation (WRFDA) system is applied to examine the impacts of assimilating MWHS-2/FY-3C with the uses of the emissivity atlas and the dynamic emissivity on the forecasts. The results indicate that the use of the dynamic emissivity retrieved from the 89 GHz channel of MWHS-2/FY-3C apparently increases the used data number for assimilation and does improve the initial fields and the 24-hour forecasts (from 0000 UTC 24 June 2016 to 0000 UTC 25 June 2016) of precipitation distribution and intensity except for the rainfall over 100 mm. But these positive impacts are not evidently better than those with the emissivity atlas. In general, these results still suggest that the future use of the dynamic emissivity in the assimilation over the complex terrain is promising.

Radar and lightning data assimilation: the impact of different setting options discussed for a heavy precipitation event occurred in Italy.

2020 ◽  
Author(s):  
Rosa Claudia Torcasio ◽  
Stefano Federico ◽  
Silvia Puca ◽  
Marco Petracca ◽  
Gianfranco Vulpiani ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Radar Reflectivity ◽  
Precipitation Event ◽  
Short Term ◽  
Term Forecast ◽  
Factor Data ◽  
The Impact ◽  
Assimilation Scheme ◽  
Reflectivity Factor

<p>The forecast of severe events at the local scale still remains challenging because of the multitude of physical processes involved on a wide range of scales. Improving the initial conditions (IC) of numerical weather prediction (NWP) models is a key point for good forecasting. Since limited-area models are nowadays operational at the kilometric scale (< 5 km), the assimilation of data from high-resolution space-time observations is crucial to correctly represent the state of the atmosphere at local scale.</p><p>Radar and lightning data are both useful to improve the IC of NWP models for several reasons. Radar data is available with a high spatio-temporal resolution and provides information on hydrometeors and wind, while lightning data locates convection both spatially and temporally accurate.</p><p>Recently, Federico et al. (2019) studied the impact of radar reflectivity factor and lightning data assimilation on the Very Short-Term Forecast (VSF) of the RAMS@ISAC NWP model for two intense precipitation events over Italy. They found that, despite an improvement of the rainfall VSF due to the assimilation of lightning and radar reflectivity factor data, the usefulness of the procedure is partially limited by the increase in false alarms, especially in case of high precipitation rates (> 50 mm/3h).</p><p>In this work, we apply the methodology proposed by Federico et al. (2019) to an intense precipitation event occurred in Italy in November 2019. The RAMS@ISAC meteorological model is used here, with a horizontal resolution of 3km.</p><p>RAMS@ISAC is initialized by a 3D-Var data assimilation scheme that uses both lightning and radar reflectivity factor data. Different 3D-Var data assimilation scheme settings are used to produce different ICs for the RAMS@ISAC model for the specific case.  The sensitivity of the precipitation field prediction to changes in these ICs will be discussed.</p><p><strong>Keywords</strong>: lightning data assimilation, radar reflectivity factor data assimilation, very short-term forecast, numerical weather prediction</p><p><strong>Reference</strong></p><p>Federico, S., Torcasio, R. C., Avolio, E., Caumont, O., Montopoli, M., Baldini, L., Vulpiani, G., and Dietrich, S.: The impact of lightning and radar reflectivity factor data assimilation on the very short-term rainfall forecasts of RAMS@ISAC: application to two case studies in Italy, Nat. Hazards Earth Syst. Sci., 19, 1839–1864, https://doi.org/10.5194/nhess-19-1839-2019, 2019.</p>

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

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 Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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 Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

2017 ◽  
Vol 21 (1) ◽  
pp. 37 ◽  
Author(s):  
Hua Deng ◽  
Yan Li ◽  
Yingchao Zhang ◽  
Hou Zhou ◽  
Peipei Cheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Least Square ◽  
Support Vector ◽  
Surface Model ◽  
Short Term ◽  
Numerical Weather

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

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