Hourly Forecast Research Articles

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

AbstractAccurate short‐term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high‐resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower‐resolution models. Recent computational advances have enabled the use of large‐eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence‐resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof‐of‐concept study on the prospect of leveraging these ultra high‐resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high‐frequency information is lost. Therefore, a statistical post‐processing approach is explored on the basis of smoothing and feature engineering from the high‐frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.

Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF‐Chem

AbstractAccuracy of cloud predictions in numerical weather models can considerably impact ozone (O3) forecast skill. This study assesses the benefits in surface O3 predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF‐Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O3 and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O3 mean bias error and 2.4 ppb in O3 root‐mean‐square‐error are obtained on average over CONUS with RAP compared to those with GFS. The WRF‐Chem simulation driven by GFS shows a higher probability of capturing O3 exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O3. The O3 concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O3 and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O3 forecast errors at the initial forecast hours especially under cloudy‐sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF‐Chem simulation with RAP produces too thick low‐level clouds, which leads to O3 underprediction in the boundary layer.

Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season

This paper examines the ability of the Weather Research and Forecasting model forecast to simulate moisture and precipitation during the North American Monsoon GPS Hydrometeorological Network field campaign that took place in 2017. A convective-permitting model configuration performs daily weather forecast simulations for northwestern Mexico and southwestern United States. Model precipitable water vapor (PWV) exhibits wet biases greater than 0.5 mm at the initial forecast hour, and its diurnal cycle is out of phase with time, compared to observations. As a result, the model initiates and terminates precipitation earlier than the satellite and rain gauge measurements, underestimates the westward propagation of the convective systems, and exhibits relatively low forecast skills on the days where strong synoptic-scale forcing features are absent. Sensitivity analysis shows that model PWV in the domain is sensitive to changes in initial PWV at coastal sites, whereas the model precipitation and moisture flux convergence (QCONV) are sensitive to changes in initial PWV at the mountainous sites. Improving the initial physical states, such as PWV, potentially increases the forecast skills.

The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter

A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous three-hourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements (RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours.

Visibility Data Assimilation and Prediction Using an Observation Network in South Korea

There have been many studies to improve visibility forecast skills using numerical models, but their performance still remains behind the forecast skills for other meteorological phenomena. This study attempted to improve visibility forecasts using a newly established automatic visibility observation network composed of 291 forward-scattering sensors in South Korea. In the analysis of recent 3-year visibility observations, clear days (visibility above 20 km) were reported for 46% of the days, and fog cases (visibility less than 1 km) accounted for 1.58% of the total observations. The Very short-range Data Assimilation and Prediction System (VDAPS) of the Korea Meteorological Administration (KMA) assimilated the visibility observations based on the Met Office Unified Model with visibility data assimilation of Clark et al. (Q J R Meteorol Soc 134:1801–1816, 2008). Prior to the data assimilation, a precipitation check eliminated visibility data with precipitation (9.4% in total, 23% for visibility less than 1 km), and a consistency check removed visibility observations that were inappropriate to relative humidity, temperature, and pressure. In a case study on two consecutive fog days, visibility forecast skills were improved by applying visibility data assimilation, mostly through modifications of aerosol concentrations. A 3-month model run in the winter of 2016 showed a positive bias in visibility predictions, especially for the low-visibility cases. Visibility data assimilation improved the prediction skills, but the positive effects were limited within 9 forecast hours and were smaller for extremely low-visibility events. Sensitivity experiments were performed using local aerosol observations with a larger number of smaller aerosol particles. Modifications in aerosol properties made better results in frequency bias for the whole forecast ranges and also improved the equitable threat score (ETS) for relatively longer forecast hours (more than 4 h).

Impact of land surface stochastic physics in ALADIN‐LAEF

AbstractTo deal with the land surface physics uncertainties, a stochastic scheme based on stochastic perturbation of physics tendencies is implemented and tested. The impact of land surface physics uncertainties and their relative importance to land surface initial uncertainties are investigated in the regional ensemble forecasting system ALADIN‐LAEF (Aire Limitée Adaptation Dynamique Développement InterNational – Limited Area Ensemble Forecasting). The land surface initial perturbation is generated by using an ensemble of land surface data assimilation; and the land surface physics uncertainties by applying the idea of stochastically perturbed parametrization tendencies (SPPT) scheme. Three experiments are conducted and compared with the reference ensemble over a 2‐month period. The results show the introduction of land surface stochastic physics increases the ensemble spread, reduces the ensemble bias, and keeps neutral in deterministic forecast skill of the ensemble, its impact strongly depending on the quality of ensemble initial conditions. The ensemble land surface data assimilation has stronger positive impact on the ALADIN‐LAEF than the land surface stochastic physics for screen‐level temperature and humidity. There is not much impact on 10 m wind and precipitation. Best results are obtained when both the ensemble land surface data assimilation and land surface stochastic physics are used simultaneously; it gives a more reliable and statistically consistent forecast, which is contributed mainly by ensemble land surface data assimilation in the first forecast hours and largely by land surface stochastic physics in the later forecast hours.

Effect of background error tuning on assimilating radar radial velocity observations for the forecast of hurricane tracks and intensities

AbstractThe background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time.

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world’s fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones.

Evaluation of Convection-Permitting Precipitation Forecast Products Using WRF, NMMB, and FV3 for the 2016–17 NOAA Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiments

Abstract During the summers of 2016 and 2017, the Center for Analysis and Prediction of Storms (CAPS) ran real-time storm-scale ensemble forecasts (SSEFs) in support of the Hydrometeorology Testbed (HMT) Flash Flood and Intense Rainfall (FFaIR) experiment. These forecasts, using WRF-ARW and Nonhydrostatic Mesoscale Model on the B-grid (NMMB) in 2016, and WRF-ARW and GFDL Finite Volume Cubed-Sphere Dynamical Core (FV3) in 2017, covered the contiguous United States at 3-km horizontal grid spacing, and supported the generation and evaluation of precipitation forecast products, including ensemble probabilistic products. Forecasts of 3-h precipitation accumulation are evaluated. Overall, the SSEF produces skillful 3-h accumulated precipitation forecasts, with ARW members generally outperforming NMMB members and the single FV3 member run in 2017 outperforming ARW members; these differences are significant at some forecast hours. Statistically significant differences exist in the performance, in terms of bias and ETS, among subensembles of members sharing common microphysics and PBL schemes. Year-to-year consistency is higher for PBL subensembles than for microphysical subensembles. Probability-matched (PM) ensemble mean forecasts outperform individual members, while the simple ensemble mean exhibits substantial bias. A newly developed localized probability-matched (LPM) ensemble mean product was produced in 2017; compared to the simple ensemble mean and the conventional PM mean, the LPM mean exhibits improved retention of small-scale structures, evident in both 2D forecast fields and variance spectra. Probabilistic forecasts of precipitation exceeding flash flood guidance (FFG) or thresholds associated with recurrence intervals (RI) ranging from 10 to 100 years show utility in predicting regions of flooding threat, but generally overpredict the occurrence of such events; however, they may still be useful in subjective flash flood risk assessment.

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices.

Comparing the Assimilation of Radar Reflectivity Using the Direct GSI-Based Ensemble–Variational (EnVar) and Indirect Cloud Analysis Methods in Convection-Allowing Forecasts over the Continental United States

Abstract Two methods for assimilating radar reflectivity into deterministic convection-allowing forecasts were compared: an operationally used, computationally less expensive cloud analysis (CA) scheme and a relatively more expensive, but rigorous, ensemble Kalman filter–variational hybrid method (EnVar). These methods were implemented in the Nonhydrostatic Multiscale Model on the B-grid and were tested on 10 cases featuring high-impact deep convective storms and heavy precipitation. A variety of traditional, neighborhood-based, and features-based verification metrics support that the EnVar produced superior free forecasts compared to the CA procedure, with statistically significant differences extending up to 9 h into the forecast. Despite being inferior, the CA scheme was able to provide benefit compared to not assimilating radar reflectivity at all, but limited to the first few forecast hours. While the EnVar is able to partially suppress spurious convection by assimilating 0-dBZ reflectivity observations directly, the CA is not designed to reduce or remove hydrometeors. As a result, the CA struggles more with suppression of spurious convection in the first-guess field, which resulted in high-frequency biases and poor forecast evolution, as illustrated in a few case studies. Additionally, while the EnVar uses flow-dependent ensemble covariances to update hydrometers, thermodynamic, and dynamic variables simultaneously when the reflectivity is assimilated, the CA relies on a radar reflectivity-derived latent heating rate that is applied during a separate digital filter initialization (DFI) procedure to introduce deep convective storms into the model, and the results of CA are shown to be sensitive to the window length used in the DFI.

Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network

Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect, renewable energy generation has become important in a modern smart grid. However, the characteristics of renewable power generation are volatile and uncertain. This work proposes a new day-ahead spatiotemporal forecasting method for solar irradiation to ensure the efficient operation of power systems. First, the frequency features of a solar irradiation time-series are extracted by discrete Fourier transform (DFT). Principal component analysis (PCA) is then used to identify the crucial frequency features, which are input to an Elman-based neural network to carry out subsequent 24-hour (day ahead) solar irradiation forecasting. Historical weather data from five weather stations that are near the target location are used. Comparative studies of traditional Autoregressive Integrated Moving Average Model (ARIMA), PCA-Back-Propagation (BP)-based neural network, the persistence method, DFT-PCA-BP and the proposed DFT-PCA-Elman method reveal that the proposed method is the most accurate in day-ahead forecasting using spatiotemporal data.

Data assimilation impact of in situ and remote sensing meteorological observations on wind power forecasts during the first Wind Forecast Improvement Project (WFIP)

AbstractDuring the first Wind Forecast Improvement Project (WFIP), new meteorological observations were collected from a large suite of instruments, including wind velocities measured on networks of tall towers provided by wind industry partners, wind speeds measured by cup anemometers mounted on the nacelles of wind turbines, and wind profiles by networks of Doppler sodars and radar wind profilers. Previous data denial studies found a significant improvement of up to 6% root mean squared error (RMSE) reduction for short‐term wind power forecasts due to the assimilation of all of these observations into the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) forecast model using a 3D variational data assimilation scheme. As a follow‐on study, we now investigate the impacts of assimilating into the RAP model either the additional remote sensing observations (sodars and radar wind profilers) alone or assimilating the industry‐provided in situ observations (tall towers and nacelle anemometers) alone, in addition to routinely available standard meteorological data sets. The more numerous tall tower/nacelle observations provide a relatively large improvement through the first 3 to 4 hours of the forecasts, which diminishes to a negligible impact by forecast hour 6. In comparison, the sparser vertical profiling sodars/radars provide an initially smaller impact that decays at a much slower rate, with a positive impact present through the first 12 hours of the forecast. Large positive assimilation impacts for both sets of instruments are found during daytime hours, while small or even negative impacts are found during nighttime hours.

Data assimilation of GPS-ZTD into the RAMS model through 3D-Var: preliminary results at the regional scale

The knowledge of water vapour distribution is a key element in atmospheric modeling and considerable information, also at the local scale, can be derived from the GPS-ZTD (global positioning system-Zenith total delay) data.This paper shows the assimilation of GPS-ZTD data into the RAMS@ISAC (Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate of the National Research Council) to improve the representation of the water vapour in the meteorological model.The data assimilation system is based on 3D-Var (three-dimensional variational assimilation system) and it is applied to a network of 29 receivers located within the Lazio Region, Central Italy. All collected data are processed using the PPP (precise point positioning) method through RTKLIB, an open source program package for GNSS (Global Navigation Satellite Systems) Positioning. Among the GPS receivers, three are single frequency receivers, able to acquire L1 frequency only, so that it is necessary a preliminary reconstruction of L2 synthetic observations, which is achieved by a new original ground-based augmentation strategy. Results show remarkably that the single frequency receivers can be used the same way as geodetic receivers.The RAMS@ISAC is run at 4 km horizontal resolution over central Italy and is nested, using one-way nesting, into a 10 km horizontal resolution run of the same model. The experiment was performed along to two months, from 28 July to 28 September 2017.Results show that the GPS-ZTD data, assimilated by 3D-Var, have an important impact on the analysis of the water vapour field and the RMSE of ZTD and IWV (vertically integrated water vapour) is roughly halved for the analysis compared to the background.The impact of the GPS-ZTD data assimilation is also evaluated for the very short term (VSF) forecast (1–3 h), obtaining an improvement of the ZTD and IWV RMSE for all three hours of forecast.

The Convective Rainfall Rate from Cloud Physical Properties Algorithm for Meteosat Second-Generation Satellites: Microphysical Basis and Intercomparisons using an Object-Based Method

The convective rainfall rate from cloud physical properties (CRPh) algorithm for Meteosat second-generation satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support of nowcasting and very short-range forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for a few hours. This letter critically discusses the theoretical basis of the algorithm with special emphasis on the empirical values and assumptions in the microphysics of precipitation, and compares the qualitative performances of the CRPh with its antecessor, the convective rainfall rate algorithm (CRR), using an object-based method applied to a case-study. The analyses show that AEMET’s CRPh is physically consistent and outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges of improving the product to an all-day algorithm are also presented.

Can we gauge forecasts using satellite-derived solar irradiance?

Satellite-derived irradiance data, as an alternative to ground-based measurements, offer a unique opportunity to verify gridded solar forecasts generated by a numerical weather prediction model. Previously, it has been shown that the mean square errors (MSE) evaluated against ground-based measurements and satellite-derived solar irradiance are comparable, which might warrant the use of satellite-based products for regional forecast verification. In this paper, the 24-h-ahead hourly forecasts issued by the North American Mesoscale forecast system are verified against both ground-based (Surface Radiation Budget Network, or SURFRAD) and satellite-based (National Solar Radiation Data Base, or NSRDB) measurements, at all 7 SURFRAD stations over 2015–2016. Three different MSE decomposition methods are used to characterize—e.g., through association, calibration, refinement, resolution, or likelihood—how well the two types of measurements can gauge the forecasts. However, despite their comparable MSEs, NSRDB is found suboptimal in its ability to verify forecasts as compared to SURFRAD. Nonetheless, if a new forecasting model produces significantly better forecasts than the benchmarking model, satellite-derived data are able to detect such improvements and make conclusions. This article comes with supplementary material (data and code) for reproducibility.

An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

Complicated weather conditions lead to intermittent, random and volatility in photovoltaic (PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is considered to be an effective tool for time-series data prediction. However, when the weather changes intensely, the long-term sequence of multivariate may cause gradient vanishing (exploding) during the training of RNN, leading the prediction results to local optimum. Long short-term memory (LSTM) network is the deep structure of RNN. Due to its special hidden layer unit structure, it can preserve the trend information contained in the long-term sequence, which is allowed to solve the problems of RNN and improve performance. An LSTM-based approach is applied for short-term predictions in this study based on a timescale that encompasses global horizontal irradiance (GHI) one hour in advance and one day in advance. Inaccurate forecasts usually occur on cloudy days, and the results of ANN and SVR in the literature prove this. To improve prediction accuracy on cloudy days, the clearness-index was introduced as an input data for the LSTM model and to classify the type of weather by k-means during the data processing, where cloudy days are classified as the cloudy and the mixed(partially cloudy). NN models are established to compare the accuracy of different approaches and the cross-regional study is to prove whether the method can be generalizable. From the results of hourly forecast, the R 2 coefficient of LSTM on cloudy days and mixed days is exceeding 0.9, while the R 2 of RNN is only 0.70 and 0.79 in Atlanta and Hawaii. From the results of daily forecast, All R 2 on cloudy days is about 0.85. However, the LSTM is still very effective in improving of RNN and more accurate than other models.

Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model

Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants. In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model. Two common ways to improve prediction accuracy are adopted. We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process. Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model. Finally, we weighted the predictions from different weather regimes to get the final results. To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically. A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method. The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application.

Between Aastha and Zee: Mystery of the Missing Market for a Weather Channel

Until a few decades ago, Doordarshan was the only channel which would broadcast TV programmes in black-&-white and that too for a few hours. It was a pure public good then, offered free of cost by the government. Today, however, from Aastha to Zee there are hundreds of dedicated private channels competing to offer news, sports, entertainment, and spirituality for a price. And still, there is not a single channel which is dedicated to 24-hour weather forecast. This is a clear case of market failure of the free enterprise system. The missing market for an exclusive weather channel is the result of perceived marginal private benefit to millions of individual farmers and other stakeholders being much less than the marginal social benefit accruing to the nation as a whole. Every year unanticipated weather patterns cause huge economic losses to food and agriculture and other industries and cause a great number of fatalities too. If IMD gives quick alerts to pilots and airports, and some private forecasters plan to give medium to long-term forecast to cricketing and other events, the same can be done for millions of farmers and other stakeholders of the economy. Therefore, government and the corporate sector may offer a 24-hour TV channel for weather forecast in the form of public private partnership (PPP). The weather forecasting infrastructure and data may come from 1 Professor and graduate student; respectively, Indian Institute of Management Ahmedabad (IIMA) government institutions such as IMD, C-DAC, and ISRO; professional content delivery and services of weathermen who deliver the content may come from TV media firms; and the break- even revenue may come through CSR activities of the corporate sector.

Assessing the predictability of WRF model for heavy rain by cold air associated with the easterly wind at high-level patterns over mid-central Vietnam

Abstract: In this study, the authors assessed the possibility of predicting precipitation due to cold air associated with the easterly wind at the high level in the WRF model with 2 days lead time for the Mid-Central Vietnam region. The results show that in the 24-hour forecasts lead time should use medium rainfall threshold (16-50 mm/day) and heavy rain (50-100 mm/day) to referent for quantitative precipitation forecast and rainfall area; for 48-hour forecast lead time should choose moderate rainfall threshold. The threshold of over 100 mm, the results of all of forecast lead time is not good, almost unpredictable. The results of this study can help forecasters have more information for forecasting rain due to cold air associated easterly wind at the high level for the Mid-Central Vietnam region.