scholarly journals Numerical Weather Prediction for Himalayan Complex Terrain: Prospects of Variational Data Assimilation

2016 ◽  
Vol 3 (1) ◽  
pp. 67
Author(s):  
Sangeeta Maharjan ◽  
Ram P. Regmi
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Environmental Research ◽  
Data Sets ◽  
Observation Data ◽  
Ongoing Research ◽  
Medium Range Weather Forecast ◽  
Research Activities ◽  
Wrf Modeling

<p>As part of the ongoing research activities at National Atmospheric Resource and Environmental Research Laboratory (NARERL) to realize high spatial and temporal resolution weather forecasts for Nepal, the Weather Research and Forecasting (WRF) modeling system performance with the National Center for Environmental Protection (NCEP) and National Center for Medium Range Weather Forecast (NCMRWF) initialization global meteorological data sets and the effect of surface observation data assimilation have been examined. The study shows that WRF modeling system reasonably well predicts the diurnal variation of upcoming weather events with both the data sets. The observation data assimilation from entire weather station distributed over the country may lead to the significant improvement in the accuracy and reliability of extended period of forecast. However, upper air observation data assimilation would be necessary to achieve desired precision and reliability of extended weather forecast.</p><p>Journal of Nepal Physical Society Vol.3(1) 2015: 67-72</p>

scholarly journals Influence of Aeolus data assimilation on the representation of gravity waves in ECMWF analysis fields

2020 ◽  
Author(s):  
Isabell Krisch ◽  
Michael Rennie ◽  
Bernd Kaifler ◽  
Sonja Gisinger ◽  
Oliver Reitebuch ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Gravity Waves ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
European Space Agency ◽  
Measurement Data ◽  
Weather Forecast ◽  
Medium Range Weather Forecast ◽  
Space Agency ◽  
Airborne Measurement

&lt;p&gt;In January 2020, the European Centre for Medium-Range Weather Forecast (ECMWF) became the first numerical weather prediction (NWP) centre to assimilate wind observations from the new European Space Agency (ESA)&amp;#8217;s Earth Explorer satellite Aeolus for operational forecasting. Aeolus was launched into space on August 22&lt;sup&gt;nd&lt;/sup&gt;, 2018, carrying the world&amp;#8217;s first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN measures profiles of line-of-sight wind components from 30km altitude down to the Earth&amp;#8217;s surface or to the level where the lidar signal is attenuated by optically thick clouds.&lt;/p&gt;&lt;p&gt;Impact assessment studies performed at ECMWF in 2019, show improved weather forecasting skills, by assimilating Aeolus wind measurements. As a side effect, these impact experiments also reveal an influence of Aeolus data assimilation on the representation of resolved gravity waves in the ECMWF model fields. Both, orographic and non-orographic gravity waves are impacted by the Aeolus data assimilation.&lt;/p&gt;&lt;p&gt;This impact of Aeolus data assimilation on the representation of gravity waves in ECMWF will be presented for selected case studies in the southern hemisphere. Ground-based and airborne measurement data from the SOUTHTRAC campaign will be used for validation where available.&lt;/p&gt;

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets

2015 ◽  
Vol 8 (4) ◽  
pp. 1789-1797 ◽  
Author(s):  
P. Vergados ◽  
A. J. Mannucci ◽  
C. O. Ao ◽  
J. H. Jiang ◽  
H. Su
Keyword(s):  
Relative Humidity ◽  
General Circulation ◽  
Intraseasonal Variability ◽  
Seasonal Fluctuation ◽  
Weather Forecast ◽  
Hadley Cell ◽  
Data Sets ◽  
Cosmic Radio ◽  
Medium Range Weather Forecast ◽  
Middle Troposphere

Abstract. The spatial variability of the tropical tropospheric relative humidity (RH) throughout the vertical extent of the troposphere is examined using Global Positioning System Radio Occultation (GPSRO) observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission. These high vertical resolution observations capture the detailed structure and moisture budget of the Hadley Cell circulation. We compare the COSMIC observations with the European Center for Medium-range Weather Forecast (ECMWF) Reanalysis Interim (ERA-Interim) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) climatologies. Qualitatively, the spatial pattern of RH in all data sets matches up remarkably well, capturing distinct features of the general circulation. However, RH discrepancies exist between ERA-Interim and COSMIC data sets that are noticeable across the tropical boundary layer. Specifically, ERA-Interim shows a drier Intertropical Convergence Zone (ITCZ) by 15–20% compared to both COSMIC and MERRA data sets, but this difference decreases with altitude. Unlike ECMWF, MERRA shows an excellent agreement with the COSMIC observations except above 400 hPa, where GPSRO observations capture drier air by 5–10%. RH climatologies were also used to evaluate intraseasonal variability. The results indicate that the tropical middle troposphere at ±5–25° is most sensitive to seasonal variations. COSMIC and MERRA data sets capture the same magnitude of the seasonal variability, but ERA-Interim shows a weaker seasonal fluctuation up to 10% in the middle troposphere inside the dry air subsidence regions of the Hadley Cell. Over the ITCZ, RH varies by maximum 9% between winter and summer.

Towards operational postprocessing of cloud cover at MeteoSwiss

2020 ◽  
Author(s):  
Stephan Hemri ◽  
Christoph Spirig ◽  
Jonas Bhend ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Cloud Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Observation Data ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Direct Model ◽  
Ensemble Model Output Statistics

&lt;p&gt;Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.&lt;/p&gt;

scholarly journals Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 489 ◽  
Author(s):  
Jürgen Helmert ◽  
Aynur Şensoy Şorman ◽  
Rodolfo Alvarado Montero ◽  
Carlo De Michele ◽  
Patricia de Rosnay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Availability ◽  
Observation Data ◽  
Climate Change Scenarios ◽  
Numerical Weather ◽  
The Future

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

2021 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Francesca Di Giuseppe ◽  
Christopher Barnard ◽  
Thomas M. Hamill ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Post Processing ◽  
Medium Range Weather Forecast ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Systematic Biases ◽  
Processing Techniques

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

scholarly journals Automatic Regional Interpretation and Forecasting System Supported by Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 793
Author(s):  
Chao Yan ◽  
Jing Feng ◽  
Kaiwen Xia ◽  
Chaofan Duan
Keyword(s):  
Machine Learning ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Model ◽  
Support Vector ◽  
Observation Data ◽  
Forecasting System ◽  
Weather Changes ◽  
Model Output Statistics

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.

scholarly journals Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015)

2016 ◽  
Vol 30 (2) ◽  
pp. 112
Author(s):  
Novvria Sagita ◽  
Rini Hidayati ◽  
Rahmat Hidayat ◽  
Indra Gustari ◽  
Fatkhuroyan Fatkhuroyan
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Dew Point ◽  
Radiosonde Data ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Condition Model ◽  
Weather Research

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy  of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o  C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation.

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