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South Asian monsoon exhibits multiscale spatiotemporal variability. Analyzing the nature and behavior of numerical weather forecast error associated with these space-time heterogeneities will eventually help in improving the models. We investigate the spatiotemporal error characteristics of the National Centre for Medium-Range Weather Forecasting (NCMRWF) Global Forecast System (NGFS) model over South Asian land and ocean separately. Although error grows with lead-time, it saturates within 3–5 days of forecast initiation. The saturated error is only about 15–25% higher than that of day-1, indicating that most of the error accumulates within first 24-h of forecast. Increase in error over oceanic regions is due to an increase in the area with high error at all precipitation ranges with large day-to-day variability. However, over land error growth is primarily confined at locations of high mean precipitation. Decomposition of error arising due to intensity and phase variations reveals that about 90% of it arises from the model’s inability to capture phase of precipitation at various timescales. We show that NGFS cannot capture synoptic scale variations ($$

In this study, the Weather Research & Forecast (WRF) model is adopted to provide mesoscale rainfall forecasts of four typical 24h duration rainstorms in North China and the DWR (Doppler Weather Radar) data is assimilated through the three-dimensional variational system (3DVar) in a cycling run mode to update the initial conditions of the WRF model. Global Telecommunication System (GTS) data produced by the National Center for Atmospheric Research (NCAR) are also assimilated as a benchmark. Before starting the assimilation process, the model spins up for 30h with the initial condition provided by the Global Forecast System. In the 1h WRF-3DVAR assimilation cycle, which means the DWR data are assimilated with a time interval of 1 h, the forecasts from the previous assimilation run serve as the background for the next run. A total of 24 runs in a cycle are performed for each 24h rainstorm. In order to highlight the contribution of the data assimilation volume, a 6h WRF-3DV AR assimilation cycle is also employed, which means, the DWR data are also assimilated with a time interval of 6 h. The results show that DWR data assimilation has improved the skill of high-intensity rainfall forecasts from the WRF model. The assimilation effect on the WRF model is not entirely decided by the amount of the assimilated data, but is closely related to the data quality.

There is a longstanding challenge in numerical weather and climate prediction to accurately model tropical wave variability, including convectively coupled equatorial waves (CCEWs) and the Madden–Julian oscillation. For subseasonal prediction, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has been shown to be superior to the NOAA Global Forecast System (GFS) in simulating tropical variability, suggesting that the ECMWF model is better at simulating the interaction between cumulus convection and the large-scale tropical circulation. In this study, we experiment with the cumulus convection scheme of the ECMWF IFS in a research version of the GFS to understand which aspects of the IFS cumulus convection scheme outperform those of the GFS convection scheme in the tropics. We show that the IFS cumulus convection scheme produces significantly different tropical moisture and temperature tendency profiles from those simulated by the GFS convection scheme when it is coupled with other physics schemes in the GFS physics package. We show that a consistent treatment of the interaction between parameterized convective plumes in the GFS planetary boundary layer (PBL) and the IFS convection scheme is required for the GFS to replicate the tropical temperature and moisture profiles simulated by the IFS model. The GFS model with the IFS convection scheme, and the consistent treatment between the convection and PBL schemes, produces much more organized convection in the tropics, and generates tropical waves that propagate more coherently than the GFS in its default configuration due to better simulated interaction between low-level convergence and precipitation.

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and $K$ -index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

The rainfall forecast of global forecasting system (GFS) model running at the India Meteorological Department (IMD), i.e., GFS T574 (25 km) and GFS T1534 (12.5 km) is verified at its natural resolution. A verification study is conducted for the rainfall forecast over Indian window during the summer monsoon season (June–September) 2016. Regional verification of rainfall forecast was also carried out over five homogeneous regions of India, i.e., north India, west coast of India, northeast India, central India and peninsular India. The skill of the model forecast is also evaluated for yes/no, categorical and few heavy rainfall cases over India. Results show that both T1534 and T574 models are able to provide more realistic spatial distribution of seasonal mean rainfall over India. This study also shows that both the model forecasts are skillful in capturing the 24 h accumulated rainfall over climatologically heavy rainfall regions, but the magnitude and location fluctuated. The rainfall forecast for different rainfall categories in terms of different statistical skill scores, i.e., ratio score, HK, bias, hit rate and HKQ showed a relatively high skill for T1534 compared to T574 over most parts of the country from the day 1 to day 5 forecasts. It is also found from this study that the skill in predicting day 1 to day 3 forecasts of both the models was found to be good and usable for all parts of the country except for high terrain regions in India. The results documented here are expected to be useful to the operational forecasters in day-to-day weather forecasting over Indian monsoon regions.

Scatterometer winds are useful in weather forecasting and cyclone monitoring related activities. The Indian Space Research Organization has launched SCATSAT-1, which is a follow-up mission of the Oceansat-2 scatterometer in September 2016. It operates in the Ku-band frequency and provides wind vector data over oceanic region from sun synchronous polar orbit. SCATSAT-1 wind vector data are validated against buoy observations, other scatterometer observations and numerical weather prediction (NWP) analysis. It is found that SCATSAT observations are of good quality in comparison with other similar types of measurements. Impact of assimilation of these observations in NWP forecasts is investigated using the National Centre for Medium Range Weather Forecasting Global Forecast System (NGFS). The NGFS model employs a three-dimensional variational-ensemble Kalman filter (3D Var-EnKF) hybrid assimilation system with a deterministic model of T-1534 resolution and 80 member ensembles of T-574 resolution. Impact of assimilation of SCATSAT-1 observations in prediction of the Kyant, Nada and Varadha cyclones formed in the Bay of Bengal region and impact on operational forecasts are discussed. Assimilation of SCATSAT observations produced marginal improvement in general NWP forecast statistics and improvement is observed in cyclone track forecasts if observations are available near the cyclone position at analysis time.

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Furthermore, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American mesoscale forecast system, the global forecast system, and the short-range ensemble forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error has been reduced by 13.80% to 61.21% when compared with three tested baselines.

Namibia is considered to be one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early-warning systems are an important aspect of adapting to climate change. Weather forecasting relies on the use of numerical weather prediction models and these need to be configured properly. In this study, we investigate the effects of using multi-nests and a convection scheme on the simulation of a heavy rainfall event over the north-western region of Kunene, Namibia. The event, which was associated with a cut-off low system, was short-lived and resulted in over 45 mm of rainfall in one hour. For the multi-nest, a 9 km grid-length parent domain is nested within the Global Forecast System (GFS) simulations, which in turn forces a 3 km grid spacing child domain. A different set of simulations are produced using a single nest of 3 km grid spacing, nested directly inside the GFS data. The simulations are produced with the convection scheme switched on and off. The impact of a single versus multi-nest is found to be small in general, with slight differences in the location of high rainfall intensity. Switching off the convection schemes results in high rainfall intensity and increased detail in the simulations, including when a grid spacing of 9 km is used. Using a grid spacing of 3 km with the convection scheme on, results in a loss of detail in the simulations as well as lower rainfall amounts. The study shows a need for different configurations to be tested before an optimum configuration can be selected for operational forecasting. We recommend further tests with different synoptic forcing and convection schemes to be conducted to identify a suitable configuration for Namibia.

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.

This paper analyses the performance of sub-optimal irrigation schedules obtained daily by solving a multi-objective optimization problem with updated weather measurements and forecasts. The approach was tested using five crops at four European locations with contrasting weather conditions. Four- and 6-day Global Forecast System (GFS) forecasts were used at all locations, and comparison with a down-scaled locally tuned model was conducted at one location. Accurate GFS temperature forecasts were observed at all four locations, but the accuracy of the potential evapotranspiration calculated from the GFS forecasts was not as consistent. Precipitations forecasts were very poor at all locations. In Greece, the down-scaled locally tuned forecasts were only marginally better than the GFS ones. In most cases, recomputing the sub-optimal irrigation schedule daily greatly reduced the impact of the imperfect weather forecasts on the final results. Using 4- or 6-day actual forecasts did not yield results appreciably better than those obtained using only historical averages as surrogate forecasts. The main consequence of the imperfect forecasts was that the final yield differed from the target one, but the (yield, irrigation) combination remained close to optimal, unless the target yield was set too high and water availability was not the main factor limiting crop development.

The Korea Institute of Atmospheric Prediction Systems (KIAPS) began a national project to develop a new global atmospheric model system in 2011. The ultimate goal of this 9-year project is to replace the current operational model at the Korea Meteorological Administration (KMA), which was adopted from the United Kingdom’s Meteorological Office’s unified model (UM) in 2010. The 12-km Korean Integrated Model (KIM) system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and a state-of-the-art physics parameterization package, has been launched in a real-time forecast framework, with initial conditions obtained via the advanced hybrid four-dimensional ensemble variational data assimilation (4DEnVar) over its native grid. A development strategy for KIM and the evolution of its performance in medium-range forecasts toward a world-class global forecast system are described. Outstanding issues in KIM 3.1 as of February 2018 are discussed, along with a future plan for operational deployment in 2020.

Improving numerical weather prediction was one of the main justifications for National Aeronautics and Space Administration’s Soil Moisture Active/Passive (SMAP) Mission. The ensemble Kalman filter (EnKF) has been extensively applied to assimilate the SM observations into numerical weather predication models. Implementation of EnKF requires the observations and model simulations to be Gaussian distributed and not biased from each other. In this letter, we tested the impacts of three bias-correction methods on effectiveness of assimilating SMAP retrievals into the National Oceanic and Atmospheric Administration—National Centers for Environmental Prediction Global Forecast System (GFS). They are: 1) global cumulative distribution function (CDF) matching with only one CDF for all grids and time series; 2) monthly CDF matching with one CDF for each grid; 3) the linear transformation technique that matches monthly mean and standard deviation of the SMAP retrievals and model simulations for each grid; and 4) assimilating SMAP SM data into GFS without any bias-correction procedure. With respect to the global land data assimilation (DA) system precipitation product, the results demonstrate that the effectiveness of assimilating SMAP retrievals into GFS is significantly impacted by the bias-correction methods. Relative to other DA cases, the monthly CDF matching produces the best precipitation forecast performance. Improvements of the three-hourly GFS precipitation prediction with SMAP assimilation using the monthly CDF matching can reach to 8% and 10% in sparsely and densely vegetated areas, respectively, and marginally positive in medium vegetation areas. Based on these results, assimilating SMAP retrievals into the GFS with the EnKF algorithm using the monthly CDF matching method is suggested for enhancing accuracy of the precipitation forecasts.

Performance optimization in the petascale era and beyond in the exascale era has and will require modifications of legacy codes to take advantage of new architectures with large core counts and SIMD units. The Numerical Weather Prediction (NWP) physics codes considered here are optimized using thread-local structures of arrays (SOA). High-level and low-level optimization strategies are applied to the WRF Single-Moment 6-Class Microphysics Scheme (WSM6) and Global Forecast System (GFS) physics codes used in the NEPTUNE forecast code. By building on previous work optimizing WSM6 on the Intel Knights Landing (KNL), it is shown how to further optimize WMS6 and GFS physics, and GFS radiation on Intel KNL, Haswell, and potentially on future micro-architectures with many cores and SIMD vector units. The optimization techniques used herein employ thread-local structures of arrays (SOA), an OpenMP directive, OMP SIMD, and minor code transformations to enable better utilization of SIMD units, increase parallelism, improve locality, and reduce memory traffic. The optimized versions of WSM6, GFS physics, GFS radiation run 70, 27, and 23 faster (respectively) on KNL and 26, 18 and 30 faster (respectively) on Haswell than their respective original serial versions. Although this work targets WRF physics schemes, the findings are transferable to other performance optimization contexts and provide insight into the optimization of codes with complex physical models for present and near-future architectures with many core and vector units.

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.

Two events of extremely heavy rainfall occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016. Due to these events, flooding occurred over east Rajasthan and affected the normal life of people. A low-pressure area lying over northwest Madhya Pradesh on 7 August 2016 moved north-westward and lay near east Rajasthan and adjoining northwest Madhya Pradesh on 8 and 9 August 2016. Under the influence of this low-pressure system, Chittorgarh district and adjoining areas of Rajasthan received extremely heavy rainfall of 23 cm till 0300 UTC of 8 August 2016 and 34 cm on 0300 UTC of 9 August 2016. A deep depression lying over extreme south Uttar Pradesh and adjoining northeast Madhya Pradesh on 19 August 2016 moved westward and gradually weakened into a depression on 20 August 2016. It further weakened into a low-pressure area and lay over east Rajasthan on 21 and 22 August 2016. Under the influence of this deep depression, Jhalawar received 31 cm and Baran received 25 cm on 19 August. On 20 August 2016, extremely heavy rainfall (EHR) occurred over Banswara (23.5 cm), Pratapgarh (23.2 cm) and Chittorgarh (22.7 cm) districts. In this paper, the performance of the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) model for real-time forecast and warning of heavy to very heavy/EHR that occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016 has been examined. The NCEP GFS forecast rainfall (Day 1, Day 2 and Day 3) was compared with the corresponding observed gridded rainfall. Based on the predictions given by the NCEP GFS model for heavy rainfall and with their application in real-time rainfall forecast and warnings issued by the Regional Weather Forecasting Center in New Delhi, it is concluded that the model has predicted the wind pattern and EHR event associated with the low-pressure system very accurately on day 1 and day 2 forecasts and with small errors in intensity and space for day 3.

In the tropics, efficient weather forecasts require high-quality vertical profiles of winds to overcome improper coupling of mass and wind fields and balance relationships in the region. The India Meteorological Department (IMD) operates the network of Doppler Weather Radar (DWR) in microwave frequencies (S-band or C-band) at various locations in India. The National Centre for Medium Range Weather Forecasting (NCMRWF) receives the volume velocity processing (VVP) wind profiles from all DWRs through the Global Telecommunication System (GTS) network in near real time. The radar VVP wind is a mean horizontal wind derived at different heights from radial velocities suitable for numerical weather prediction applications. Three numerical experiments, CNTL (without VVP winds), 3DVAR and HYBRID with the assimilation of VVP winds by means of 3-dimensional variational (3dvar) and hybrid data assimilation systems were conducted using the NCMRWF Global Forecast System (NGFS) model. This study had two objectives: (1) quality assessment of VVP winds and (2) investigation of the impact of VVP wind profiles on NGFS model forecast. The quality of VVP wind profiles was assessed against the NGFS model background and radiosonde wind profiles. The absolute values of zonal and meridional wind observation minus background (O−B) increased with the pressure for all DWRs. All radars exhibited the accepted (rejected) ratio as a decreasing (increasing) function of pressure. The resemblance between the zonal and meridional O−B statistics for 3DVAR and HYBRID experiments is apparently remarkable. The accepted VVP winds and radiosonde winds in both experiments (3DVAR and HYBRID) were consistent. The correlation coefficient (R) was higher at Patna (Patiala) for zonal (meridional) winds in the 3DVAR experiment and at Patna (Jaipur) in the HYBRID experiment. At Chennai, the R value was lower in both the experiments for both wind components. However, because of the assimilation of VVP winds by means of 3dvar, the root-mean-square error (RMSE) of zonal and meridional winds improved by approximately 2–3% up to the day 5 forecast in the analysis performed below 700 hPa. Further improvement in RMSE by approximately 5% was observed in both the wind components because of the hybrid data assimilation. The zonal and meridional wind RMSEs in the HYBRID (3DVAR) experiment improved by ≈5 (3)% compared with the 3DVAR (CNTL) experiment.

Solar panel photovoltaic (PV) systems are widely used in Korea to generate solar energy, which is one of the most promising renewable energy sources. With regard to solar electricity providers and a grid operator, it is critical to accurately predict solar power generation for supply-demand planning in an electrical grid, which directly affects their profit. This prediction is, however, a challenging task because solar power generation is weather dependent and uncontrollable. In this study, a daily prediction model based on the weather forecast information for solar power generation is proposed. In the case of the proposed model, the cloud and temperature data available from the weather forecast information is used to predict the amount of solar radiation as well as a loss adjustment factor to reflect the possible loss of power generation due to the degradation or failure of the PV module. Using the proposed model, solar power generation for the following day can be predicted. The proposed model is embedded into a solar PV monitoring system that is commercially used in Korea, and it is shown to perform better than the existing prediction models.

Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Though significant scientific improvements took place in the global models for weather forecasting, still they are not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecasts Systems (GFS) are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System (GEFS) and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/ calibration data set used in the study. We find promising results and emphasize to implement such data driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

The Lagrangian particle dispersion model FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modelling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g. greenhouse gases, short-lived climate forcers like black carbon, or volcanic emissions, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.3, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS), and data from the United States' National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physico-chemical parametrizations, input/output formats and available pre- and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes. The deviation from 100 % efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g. to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option for running backward in time from atmospheric concentrations at receptor locations since many years, but this has now been extended to work also for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing of FLEXPART output data and briefly report on alternative FLEXPART versions.

The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g., greenhouse gases, short-lived climate forcers like black carbon and volcanic ash, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to global. In particular, inverse modeling based on source–receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100 % efficiency is almost entirely due to the remaining nonparallelized parts of the code, suggesting large potential for further speedup. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g., to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option of running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to also work for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, the temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing for more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing FLEXPART output data, and we briefly report on alternative FLEXPART versions.

The aim of this paper is to present a deep neural network architecture and use it in time series weather prediction. It uses multi stacked LSTMs to map sequences of weather values of the same length. The final goal is to produce two types of models per city (for 9 cities in Morocco) to forecast 24 and 72 hours worth of weather data (for Temperature, Humidity and Wind Speed). Approximately 15 years (2000-2015) of hourly meteorological data was used to train the model. The results show that LSTM based neural networks are competitive with the traditional methods and can be considered a better alternative to forecast general weather conditions.

In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.

Meteorologists process and analyze weather forecasts using visualization in order to examine the behaviors of and relationships among weather features. In this design study conducted with meteorologists in decision support roles, we identified and attempted to address two significant common challenges in weather visualization: the employment of inconsistent and often ineffective visual encoding practices across a wide range of visualizations, and a lack of support for directly visualizing how different weather features relate across an ensemble of possible forecast outcomes. In this work, we present a characterization of the problems and data associated with meteorological forecasting, we propose a set of informed default encoding choices that integrate existing meteorological conventions with effective visualization practice, and we extend a set of techniques as an initial step toward directly visualizing the interactions of multiple features over an ensemble forecast. We discuss the integration of these contributions into a functional prototype tool, and also reflect on the many practical challenges that arise when working with weather data.

This paper describes a stochastic analytical model for predicting airport capacity with a look-ahead horizon suitable for strategic traffic flow management. The model extends previous research on airport capacity estimation by explicitly integrating the impact of terminal weather and its uncertainty. Different types of weather forecast inputs are explored, including deterministic forecasts, deterministic forecasts with forecast error models, and ensemble forecasts, to produce distributions of predicted arrival and departure capacity for each runway configuration at an airport. The paper introduces a mathematical capacity prediction model and weather data sources supported by a proof-of-concept prototype implementation, including results of validation studies at Hartsfield–Jackson Atlanta International Airport. These results are compared with standard airport benchmark capacities and actual observed throughputs. Results show that our analytical airport capacity model accurately predicts maximum available a...

Accurate wind speed forecasting, which strongly influences the safe usage of wind resources, is still a critical issue and a huge challenge. At present, the single-valued deterministic NWP forecast is primarily adopted by wind farms; however, recent techniques cannot meet the actual needs of grid dispatch in many cases. This paper contributes to a new multi-step forecasting method for operational wind forecast, 96-steps of the next day, termed the CS-FS-WRF-E model, which is based on a Weather Research and Forecasting (WRF) ensemble forecast, a novel Fuzzy System, and a Cuckoo Search (CS) algorithm. First, the WRF ensemble, which considers three horizontal resolutions and four initial fields, using a 0.5° horizontal grid-spacing Global Forecast System (GFS) model output, is constructed as the basic forecasting results. Then, a novel fuzzy system, which can extract the features of these ensembles, is built under the concept of membership degrees. With the help of CS optimization, the final model is constructed using this evolutionary algorithm to adjust and correct the results obtained based on physical laws, yielding the best forecasting performance and outperforming individual ensemble members and all of the other models for comparison.

Background: Numerical weather and climate models rely on the use of microphysics schemes to simulate clouds and produce precipitation at convective scales. It is important that we understand how different microphysics schemes perform when simulating high impact weather to inform operational forecasting. Methods: Simulations a heavy rainfall event from 17-20 February 2017 over Botswana were made with the Weather Research and Forecasting (WRF) model using four different microphysics schemes. The schemes used were the Weather Research and Forecasting Single Moment 6-class scheme (WSM6); Weather Research and Forecasting Single Moment 5-class scheme (WSM5); Stony Brook University scheme (SBU-YLIN); and Thompson scheme. WSM5 is considered as the least sophisticated of the four schemes, while Thompson is the most sophisticated. Simulations were initialized and forced by the Global Forecast System (GFS), and configured with a grid spacing of 9km over an outer domain and 3km for a nested inner domain without the convection parameterization. The simulations were produced using the University of Botswana and the Centre for High Performance Computing (CHPC) High Performance Computing (HPC) systems. Results: WSM5 and WSM6 simulations are mostly similar; the presence of graupel in WSM6 did not result in large differences in the rainfall simulations. SBU-YLIN simulated the least amount of rainfall, followed by Thompson. All the schemes captured the north-south rainfall gradient observed on 17 February, but with all simulations rainfall is simulated slightly south of where it was observed. All the schemes overestimated rainfall on 18 February over the central parts of Botswana, and underestimated rainfall on 19 February over most of Botswana. Conclusions: Simulations with different microphysics looked more similar to each other, than to observations. Future studies will test WRF configurations including a single nest over Botswana to determine the best configuration for operational forecasting by the Botswana Department of Meteorological Services.

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

Many predictive control problems can be solved at lower cost if the practitioner is able to make use of a high-dimensional forecast of exogenous uncertain quantities. For example, power system operators must accommodate significant short-term uncertainty in renewable energy infeeds. These are predicted using sophisticated numerical weather models, which produce an ensemble of scenarios for the evolution of atmospheric conditions. We describe a means of incorporating such forecasts into a multistage optimization framework able to make use of spatial and temporal correlation information. We derive an optimal procedure for reducing the size of the look-ahead problem by generating a low-dimensional representation of the uncertainty, while still retaining as much information as possible from the raw forecast data. We then demonstrate application of this technique to a model of the Great Britain grid in 2030, driven by the raw output of a real-world high-dimensional weather forecast from the U.K. Met Office. We also discuss applications of the approach beyond power systems.

This study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.

Weather prediction is a real time challenging issue witnessed by the world in the last decade. The prediction is becoming more complex due to the ever changing weather conditions. Many models have been discussed for predicting the weather data assuming the related attributes as independent variables. For effective analysis of the weather, it is necessary to understand various influencing factors that cause the weather changes. It is therefore necessary to identify the relationship between these attributes for better understanding of the weather data. In this article, a weather prediction model based on the spatial and temporal dependencies among the climatic variables together with forecasting analysis.

Big Data is spreading vastly in the industry. Most of the industries want to have the records of not only the work they do but also are eager to know the taste of the consumer. Big Data is becoming relative to almost all aspects of human activity from just recording events to research, design, production and digital services or products delivery to the final consumer. In this work, application of big data is investigated in the field of weather prediction. The weather prediction data is generated from various sources such as radar, ships, ground observation etc. It contains certain useful and useless information for prediction of weather data and in the form of unstructured data. Further, in this work, Hadoop framework is applied to process this unstructured data. The word count algorithm is being used to find the overall condition of that day. Further fuzzy logic (FL) and artificial neural network fuzzy interface system (ANFIS) methods are investigated for accurate prediction of weather data on the basis of mean square error. Experimental results show that the ANFIS method gives more accurate results in comparison to other methods being compared.

Software has been developed to evaluate National Weather Service spot forecasts. Fire management officials request spot forecasts from National Weather Service Weather Forecast Offices to provide detailed guidance as to atmospheric conditions in the vicinity of planned prescribed burns as well as wildfires that do not have incident meteorologists on site. This open source software with online display capabilities is used to examine an extensive set of spot forecasts of maximum temperature, minimum relative humidity, and maximum wind speed from April 2009 through November 2013 nationwide. The forecast values are compared to the closest available surface observations at stations installed primarily for fire weather and aviation applications. The accuracy of the spot forecasts is compared to that available from the National Digital Forecast Database (NDFD). Spot forecasts for a selected prescribed burn are used to illustrate issues associated with the verification procedures. Cumulative statistics for National Weather Service County Warning Areas and for the nation are presented. Basic error and accuracy metrics for all available spot forecasts and the entire nation indicate that the skill of the spot forecasts is higher than that available from the NDFD, with the greatest improvement for maximum temperature and the least improvement for maximum wind speed.

Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scale-dependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to re-introduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a two-week long period in September 2012. It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently re-introduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

High-resolution Global Forecast System (GFS) model products are used to detect the earliest tropical cyclogenesis (TCG) signature over the North Indian Ocean (NIO). In order to identify the formation of tropical cyclones (TCs) in the model forecast fields, three parameters, namely, low-level relative vorticity, genesis potential parameter, and wind circulation pattern matching index, are examined in this paper. The detection of TCG in the GFS model is performed for 14 TCs that developed in the NIO during the 2008-2011 period. The results show that the wind circulation pattern matching index provides the most reliable guidance (high probability of detection, i.e., approximately 0.92, in a 24-h forecast with relatively few false alarms) for identifying TC formation over the NIO.

Probabilistic quantitative precipitation forecasts challenge meteorologists due to the wide variability of precipitation amounts over small areas and their dependence on conditions at multiple spatial and temporal scales. Ensembles of convection-allowing numerical weather prediction models offer a way to produce improved precipitation forecasts and estimates of the forecast uncertainty. These models allow for the prediction of individual convective storms on the model grid, but they often displace the storms in space, time, and intensity, which results in added uncertainty. Machine learning methods can produce calibrated probabilistic forecasts from the raw ensemble data that correct for systemic biases in the ensemble precipitation forecast and incorporate additional uncertainty information from aggregations of the ensemble members and additional model variables. This study utilizes the 2010 Center for Analysis and Prediction of Storms Storm-Scale Ensemble Forecast system and the National Severe Storms Laboratory National Mosaic & Multi-Sensor Quantitative Precipitation Estimate as input data for training logistic regressions and random forests to produce a calibrated probabilistic quantitative precipitation forecast. The reliability and discrimination of the forecasts are compared through verification statistics and a case study.

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.

The extremely damaging Hurricane Sandy (2012) is noteworthy for the significant track bifurcation among several forecast models approximately 6–7 days before landfall. The operational versions of the European Center for Medium-Range Weather Forecasting (ECMWF) and Global Forecast System (GFS) models exemplify this difference over their runs early in Sandy's life cycle, as the former routinely forecast a storm which would make a U.S. landfall, while the latter routinely forecast a track toward the central North Atlantic. This was also generally true of their respective ensemble members. This paper demonstrates that these differences were caused not by resolution or initial condition differences but rather due almost exclusively to choice of cumulus parameterization. Simulations performed with the Weather Research and Forecasting model using an ECMWF-like cumulus parameterization in conjunction with GFS initial conditions yield forecasts whose accuracy is similar to that of ECMWF forecasts at extended time ranges (up to 1 week before landfall).

SIMET is an original weather environment simulator, developed to support demonstrations and engineering studies of future flight management functions. Backed by a large database of actual weather observations and forecasts, it has the capability to simulate very realistic weather conditions, covers most of the extreme weather situations that can be encountered in real operations and validates the robustness of the studied solutions. SIMET can be interfaced with existing flight simulators using a dedicated message-oriented protocol. It can also be used to select and extract weather datasets for ingestion by engineering model simulations offline.

Recent research has shown that assimilating satellite soil moisture (SM) retrievals into the land surface models (LSMs) improves simulations of land-atmosphere water and energy exchanges. With satellite SM retrievals becoming widely and continuously available, it is desirable to examine the impact of assimilating them into numerical weather prediction models in order to improve numerical weather forecast skills. Based on the development of the coupled system of National Centers for Environmental Prediction (NCEP)-Global Forecast System (GFS) and National Aeronautics and Space Administration (NASA)-Land Information System (LIS) in this paper, we designed an experiment to demonstrate the impacts of assimilating the Advanced Scatterometer (ASCAT) SM data products on the weather forecasts of GFS. With respect to the surface air temperature analysis product of National Oceanic and Atmospheric Administration (NOAA)-Climate Prediction Center (CPC) and CPC’s morphing method-based precipitation data, improvement from the ASCAT SM assimilation for probabilities of high quality forecasts can reach up to 1.7% for GFS precipitation, 3.1% for 2-meter minimum temperature, and 3.1% for 2-meter diurnal temperature range predictions, respectively. These results suggest that satellite SM data assimilation could be beneficial for GFS numerical weather forecasts of NOAA NCEP.

5 Abstract: Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. Weather prediction approaches are challenged by complex weather phenomena with limited observations and past data. Weather phenomena have many parameters that are impossible to enumerate and measure. Increasing development on communication systems enabled weather forecast expert systems to integrate and share resources and thus hybrid system has emerged. Even though these improvements on weather forecast, these expert systems can't be fully reliable since weather forecast is main problem.

Great waves are usually introduced by and accompanied with strong winds; either of them is dangerous to the vessels on the sea. With the help of quantitative wind speed forecast, a seaman or a self-control system can decide whether to go along the original navigation route or switch to a safer one. A new 24/7 auto-response system was developed with the purpose of providing the future 1-5 days nautical surface wind trend per vessel's request. To lower the satellite communication cost, the messages are formatted in concise and quantitative text-mode (total < 200 bytes). The weather forecast data, retrieved at the nearest half degree grid point from an advanced global weather numerical model stored in a US server, is adjusted by statistical rending method according to the local conditions. The preliminary results were proven to contain great reliability and creditability.

This paper presents the results of a forecast skill score comparison for the popular two semester weather forecast game played at University of Missouri, Columbia, MO, United States, for a total of 106 days, during the autumn 2006 and winter 2007 semesters. A relatively less experienced/first time student forecaster (SF) skill, based upon the funnel approach to weather forecasting, is compared with the then state-of-the-art mesoscale operational numerical weather prediction (NWP) model outputs and the observations. Several measures of the forecast skills are employed to illustrate the intercomparison of the different aspects of performance of the game players for a conventional forecast game setup in an educational environment, after paying particular attention to the associated sampling uncertainty in the analysis of the forecast game. Bootstrap resampling based confidence intervals are computed and compared for the SF and NWP model forecasts and the observations to quantify the relative accuracies of the forecasts. The SF performed quite comparable to the NWP models, and better than the climatology and the persistence, for the next-day forecasts of temperature and precipitation, when the forecast skill scores of the game players were averaged over the period of the forecast game. These observations support the judgement that the contribution of human forecasters, even in the contemporary era of progressively improving and fully automated NWP model outputs, remained a crucial one.

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

Atmospheric predictability has improved by approximately 1 day per decade during the last 20 years based on verification results of ECMWF forecast output. In Finland, locally applied accuracy measures indicate marked improvements in the quality of forecasts for the general public since the late 1980s. It is assumed that similar trends will continue to the foreseeable future. Use of weather information will allow for better options in the decision-making of various stake holders in the transport sector, such as commuters or tourists, transport infrastructure owners and transport service and maintenance operators. This paper discusses the economic impacts and value of weather forecasts on different transport modes (road, rail, air) highlighting the effects of potential improvements in forecast quality in the expected future climates in Europe. It is not only the improved quality of available weather forecasts that will define the value of information. The way in which the information is communicated and how it is being utilized by decision-makers are highly relevant steps in a weather service value chain. Rather than applying the traditional Cost-Loss model, which would relate improved forecast accuracy to increased expected utility, an alternative approach is being applied. This ‘Weather Service Chain Analysis’ (WSCA) accounts for imperfect features in the communication chain and in the use of weather information by analysing the decay of the total potential benefits via decomposing the information flow from original forecast generation to final benefit realization. Concrete estimates are provided for the road transport modes both in Finland and in Europe. Copyright © 2013 Royal Meteorological Society

A deeper understanding of how human activity-travel behavior is affected by various weather conditions is essential for both policy makers and traffic managers. To unravel the ambiguity in findings reported in the literature, the main objective of this paper is to obtain an accurate assessment of how weather forecasts trigger changes in Flemish activity-travel behavior. To this end, data were collected by means of a stated adaptation experiment, which was administered both on the Internet and via traditional paper-and-pencil questionnaires. To address the main research question of this paper, two statistical techniques were adopted. The first technique is the computation of Pearson chi-square independence tests. The second approach is the estimation of a GEE-MNL-model. The results from both techniques underscore the dual role of weather forecasts on changes in activity-travel behavior. On the one hand, the results clearly illustrate the significant effect of forecasted weather; the likelihood of changes in activity-travel behavior significantly depends on the weather forecasted. On the other hand, different methods of acquiring weather information (exposure, media source, or perceived reliability) do not impact the probability of behavioral adaptations. This duality may be partially attributable to the discrepancy that exists between weather forecasts and true traffic and roadway conditions. Therefore, the implementation of a road weather information system that is directly linked to the weather forecasts is recommended.

The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia. The models displayed errors of superestimation and underestimation with respect to the observed precipitation, mainly over center-north of Para and all of Amapa, where the precipitation is higher. Among the analyzed models, GFS shows the best performance, except during January and March, when the model to underestimated precipitation, possibly due to the anomalously high values recorded.

Background/Objectives: To forecast weather, which is one of the greatest challenges in meteorological department. Weather prediction is necessary so as to inform people and prepare them in advance about the current and upcoming weather condition. This helps in reduction in loss of human life and loss of resources and minimizing the mitigation steps that are expected to be taken after a natural disaster occurs. Methods/Statistical analysis: This study makes a mention of various techniques and algorithms that are likely to be chosen for weather prediction and highlights the performance analysis of these algorithms. Various other ensemble techniques are also discussed that are used to boost the performance of the application. Findings: After a comparison between the data mining algorithms and corresponding ensemble technique used to boost the performance, a classifier is obtained that will be further used to predict weather. Applications: Used to Predict and forecast the weather condition of specific region based on the available pre historical data which helps to save resources and prepare for the changes forth coming.

Mesoscale models generate output for many applications worldwide. Normally, initialization of a mesoscale model employs the most recent output available from a larger-scale regional or global model. Such output also is used to adjust the lateral boundary conditions as the larger-scale model evolves. However, some applications, especially in remote or minimally connected areas, may require the use of large-scale model data that may be one or more days old. The analysis of this report investigates the effect of using older large-scale data on mesoscale model accuracy. The report briefly describes the procedure and presents the results of a preliminary analysis. As expected, accuracy generally tended to decline as the large-scale data aged, but appeared to improve slightly as the age of the large-scale data increased from 3 days old to 4 days old. Also, there is a wide variation between individual cases. The change in meteorological variables is explored, but the emphasis is on the effect on simulated artillery trajectories. The use of trajectories with their major dependence on meteorological conditions provides a means to check the net overall ability of a model to simulate the atmosphere over the space and time covered by the trajectory.

NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31st, 2015 and has been providing global soil moisture (SM) data products since April 2015. One of the primary justifications of the mission was to improve numerical weather predictions. With the SMAP SM data becoming available, it is anxiously expected that SMAP SM data could be demonstrated to significantly improve weather forecasts from numerical weather prediction (NWP) models. In this study, the NOAA Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF) model coupled with NASA Land Information System are used to carry out the demonstration. A hardwired Ensemble Kalman filter is implemented within GFS to assimilate surface SM observations. For assimilating SM data into NUWRF model, NASA Land Information System (LIS) is coupled with the NUWRF model. In this paper preliminary results of SMAP soil moisture impact on GFS and NUWRF forecasts are presented after the assimilation algorithms and system designs are introduced. Plans for more comprehensive assessment of the satellite soil moisture data impact on NWP models will be discussed.