Accuracy Of Precipitation Forecasts Research Articles

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Advancements in daily precipitation forecasting: A deep dive into daily precipitation forecasting hybrid methods in the Tropical Climate of Thailand

Climate change and increasing water demands underscore the importance of water resource management. Precise precipitation forecasting is critical to effective management. This study introduced a Daily Precipitation Forecasting Hybrid (DPFH) technique for central Thailand, which uses three different input-based models to improve prediction accuracy.•The proposed methods precisely combine the biorthogonal wavelet transformation (BWT) function through BWT-RBFNN (Radial Basis Function Neural Networks) and (BWT-LSTM-RNN)Long Short-Term Memory Recurrent Neural Networks. Comparative analyses reveal that hybrid models perform better than conventional deep LSTM-RNN and Multilayer Perceptron Artificial Neural Networks (MLP-ANN). Although MLP-ANN showed moderate effectiveness, LSTM-RNN displayed notable enhancements, particularly evidenced by an impressive R2 (0.96) in Model M-2.•The combination of BWT-LSTM-RNN yielded substantial enhancements, constantly surpassing standalone models. Specifically, DPFH-3 exhibited superior performance across multiple observation stations.•The findings emphasize the efficiency of the BWT-LSTM-RNN models in capturing varied precipitation patterns, highlighting their potential to significantly improve the accuracy of precipitation forecasts, particularly in the context of water resource management in central Thailand.

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.

Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution

Statistical post-processing is a pivotal approach in enhancing the statistical accuracy and applicability of precipitation forecasts from numerical weather prediction models. The existing methods primarily focus on correcting errors in either categorical or quantitative aspects individually, failing to address both simultaneously. Furthermore, the benefits of post-processing in extending the effective lead time (ELT) have not been quantitatively examined. In this study, an integrated statistical post-processing method by combining the Empirical Quantile Mapping (EQM) and the Bernoulli-Gamma-Gaussian model (BGG) (EQM-BGG for short) is constructed, and a comprehensive accuracy metric for evaluating the ELT is also proposed. Taking the Bengbu Basin located in the upper and middle reaches of the Huai River Basin as the study area, the statistical post-processing using the three methods (EQM, BGG, EQM-BGG) is performed on the multi-model weighted integration forecast precipitation (MWIFP), which are derived from three numerical models, namely ECMWF, NCEP and CMA. The results demonstrate that the integration of EQM-BGG capitalizes on the combined strengths of EQM and BGG, facilitating a dual correction of categorical and quantitative errors. The forecast accuracy (FA) and mean absolute error (MAE) of the post-processed basin-average forecasts are enhanced by more than 10%, and the ELTs for both the entire basin and sub-basins are extended by 12–78 h. At the grid scale, EQM-BGG significantly reduces the decay rate of forecast accuracy and the expansion speed of low-accuracy regions as the lead time increases, the improvement of FAs and MAEs surpasses 10% and 20%, respectively, across all grids within the lead time of 96–102 h. Furthermore, certain grids in the northwestern part show an extension of ELTs by 18 to 54 h. These findings highlight that EQM-BGG significantly enhances both categorical and quantitative accuracy simultaneously, resulting in precipitation forecasts that exhibit greater reliability over longer lead times. This advancement enriches the methodologies for hydro-meteorological forecasts.

TAFFNet: Time‐Aware Adaptive Feature Fusion Network for Very Short‐Term Precipitation Forecasts

AbstractDeep learning models based on radar echo extrapolation have been widely used in precipitation nowcasting. However, they face the challenge of insufficient input information when extending the forecast lead time, requiring the incorporation of physics‐based numerical weather prediction (NWP). Given that the strengths of radar and NWP data vary depending on the forecast time, effectively fusing these two data sources in a unified deep learning model remains an open research problem. In this study, we propose a Time‐aware Adaptive Feature Fusion Network (TAFFNet) for very short‐term precipitation forecasts up to 12 hr. TAFFNet fuses features adaptively according to their relative contributions to forecast skill at different times. Experimental results demonstrate that TAFFNet performs the best for very short‐term precipitation forecasts. The case studies show that adaptively fusing NWP with radar can improve the accuracy of precipitation forecasts, especially for predicting the initiation and dissipation of storms at longer lead times.

Accuracy of precipitation forecasts: finding the right threshold for what is considered rain

Accuracy of precipitation forecasts: finding the right threshold for what is considered rain

A support vector machine-based method for improving real-time hourly precipitation forecast in Japan

• Improved real-time hourly precipitation forecast accuracy using machine learning. • Investigated the use of CDF-transform for short-range forecast bias correction. • Provided forcing data for the next step to enhance flood early warning accuracy. Real-time precipitation forecast facilitates water management and water-associated disaster early warning. However, numerical weather prediction (NWP) models provide precipitation forecasts with bias. This study proposed to combine support vector machine (SVM) regression with quantile-based bias correction method to improve real-time 39-hour precipitation forecasts in Japan. Five methods were compared and evaluated against observations, which include SVM regression, quantile mapping (QM), cumulative distribution function transform (CDFt), and the combination of SVM and QM (or CDFt). Results indicated that the combination of SVM and CDFt (i.e., SVM-CDFt) generally provided the highest accuracy with good computational efficiency. SVM alone improved the spatial representation of hourly precipitation with a correlation coefficient increased from 0.387 to 0.490 in January and from 0.235 to 0.296 in July in the cross-validation experiment. However, SVM underestimated the variability of hourly precipitation and heavy precipitation events. QM and CDFt perform well in correcting the bias in modeled precipitation, while they have limited capability in correcting the rainband location. Combining SVM and quantile-based method took advantage of both approaches, providing a more consistent variability with observations and better predicted extreme precipitation events, although overestimation of rainfall area was witnessed. The simple concept, high computational efficiency, as well as evident improvement in forecast accuracy make the combined cases, especially the SVM-CDFt method, beneficial for real-time precipitation forecast and flood early warning.

The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin

Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88).

Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast

Radar data are assimilated into numerical prediction models to improve precipitation forecasts using three-dimensional (3D) variational (VAR) and hybrid data assimilation (DA) methods. The effect of radar DA (RDA) and prediction accuracy according to DA methods are evaluated by conducting experiments of three heavy rainfall events and analyzing the spatial pattern of accumulated precipitation, profiles of hydrometeors, and cloud microphysical processes. Increments of the initial analysis revealed that the hybrid method simulated the convective band, wind convergence in front of the cold front and a higher amount of water vapor mixing ratio compared to 3DVAR. Based on numerical experiments, the root mean square error of the total cumulative precipitation in 3DVAR was 9.68 mm lower than that of the no-RDA experiment (CTRL), whereas that of the Hybrid was 11.12 mm lower than that of CTRL. RDA improved precipitation forecasts, and Hybrid showed better precipitation forecast accuracy than 3DVAR. Although RDA indirectly changed the water vapor, rain, snow, and graupel mixing ratios, the change in the water vapor amount was the major factor affecting microphysical processes. The increase (decrease) in the water vapor mixing ratio had the greatest impact on precipitation formation (dissipation), whereas the effect of snow, rain, and graupel mixing ratios was relatively small. The results indicate that it is critical to create an environment in which water vapor can transform into precipitating hydrometeors and the hydrometeors can grow through cloud microphysical processes. The accuracy of heavy rain forecast can be improved by hybrid DA method, which can consider the model error in real time, making it more adaptable to the day-to-day changing weather conditions.

Medium- and Long-Term Precipitation Forecasting Method Based on Data Augmentation and Machine Learning Algorithms

Accurate medium and long-term precipitation forecasting plays a vital role in disaster prevention and mitigation and rational allocation of water resources. In recent years, there are various methods for medium- and long-term precipitation forecasting based on machine learning algorithms. However, machine learning has a high demand for the size of sample data. Therefore, this article proposes a data augmentation algorithm based on the K-means clustering algorithm and synthetic minority oversampling technique (SMOTE), which can effectively enhance sample information. Besides, through constructing random forest (RF), extreme gradient boosting (XGB), recurrent neural network (RNN), and long short-term memory (LSTM) are, respectively, constructed as the models to forecast monthly grid precipitation of the Danjiangkou River Basin. This study aims to improve the accuracy of medium- and long-term precipitation forecasting. The main results are the following two aspects: 1) in most years, the anomaly correlation coefficient and Pg score of SMOTE-km-XGB and SMOTE-km-RF exceed that of XGB and RF; furthermore, compared with the other three methods, SMOTE-km-XGB method is more suitable for precipitation forecasting in the studied basin in this article; and 2) the forecasting results of two deep learning methods (RNN and LSTM) show that the sample data processed by the K-means clustering algorithm and SMOTE data augmentation algorithm have not achieved considerable results in deep learning. This study improves the accuracy of precipitation forecast by expanding and balancing the information of sample data, and provides a new research idea for improving the accuracy of medium- and long-term hydrological forecasting.

Urban flood forecasting based on the coupling of numerical weather model and stormwater model: A case study of Zhengzhou city

Study regionUrban built-up area of Zhengzhou, China. Study focusA coupled urban flood forecasting model based on WRF-SWMM is proposed. Different spatial resolution data as input for WRF (Weather Research and Forecasting Model) to study the characteristics of forecasting precipitation, combined with measured precipitation series, the SWMM (Storm Water Management Model) was driven to forecast the urban flood process. New hydrological insights for the study regionUsing the WRF-SWMM coupling model to simulate flood disasters can effectively improve the timeliness of urban flood prediction. The results indicate that a spatial resolution of 0.25-degree improved the overall precipitation prediction results. With an increase in lead time, the prediction performance of the WRF decreased. The WRF model can simulate results effectively owing to its sensitivity to bimodal rainfall patterns. When the WRF model produces an overestimate (or underestimate), part of the urban canals in the SWMM model will also carry out a high forecast (or low forecast) output of flood peak discharge. The SWMM model also has a cumulative amplification effect on the results of flood simulation. In addition, the quantitative accuracy of precipitation forecasts plays a vital role in urban flood simulation, and the existing coupling models based on WRF-SWMM can generate reasonable forecasts 3–6 h in advance.

Improving numerical forecast of the rainstorms induced by Mongolia cold vortex in North China with the frequency matching method

Mongolia cold vortex is an important synoptic system causing summer convective precipitation in North China and the numerical model shows poor performances for forecasting the convective rainstorms. It is, therefore, urgent to improve the forecast skill of the rainstorms induced by Mongolia cold vortex. Taking the Haihe River Basin as an example and using the observational precipitation data monitored by automatic meteorological stations and the forecast precipitation data from European Centre for Medium-Range Weather Forecasts (ECMWF), this study is the first time to recombine the numerical forecast series that have the greatest similarity to the observed precipitation by using correlation analysis on a sliding time window. We then corrected the recombinant forecast series by using frequency matching method (FMM) and finally established a correction model for improving precipitation forecast accuracy. Results revealed that FMM could improve the precipitation forecast skill for 1-3 d lead-time. Compared with light, moderate, and heavy rain, the improvement effects are more obvious for rainstorm (50–99.9 mm) and heavy rainstorm (≥100 mm). Moreover, the calibrated forecasts have good performances to improve the accuracy for most magnitudes but not spatial distributions. These findings demonstrate that FMM has good performance for correcting precipitation forecasts induced by Mongolia Cold Vortex and thus can be used to improve the forecast skill of summer rainstorms, providing more accurate forcing inputs for hydrologic model to improve flooding forecast skill in North China.

Availability analysis of precipitation forecast results in reservoir flood control operation in Anqiu

Anqiuis located in the Weihe River basin. The precipitation is mainly concentrated in the flood season. There are more than 100 large, medium and small reservoirs such as Shangzhuang reservoir and Moushan reservoir. It is of great significance for reservoir operation and flood control decision-making to study the accuracy of precipitation forecast in flood season in Anqiu and to analyse the availability of the forecast results.

Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting

Short-term precipitation forecast in local areas based on radar reflectance images has become a hot spot issue in the meteorological field, which has an important impact on daily life. Recently, deep learning techniques have been applied to this field, and the effect is promoted remarkably compared with traditional methods. However, existing deep learning-based methods have not considered the problem that different areas and channels exert different influence on precipitation. In this paper, we propose to incorporate the multihead attention into a dual-channel neural network to highlight the key areas for precipitation forecast. Furthermore, to solve the problem of excessive loss of global information caused by the attention mechanism, the residual connection is introduced into the proposed model. Quantitative and qualitative results demonstrate that the proposed method achieves the state-of-the-art precipitation forecast accuracy on the radar echo dataset.

Improving monthly streamflow forecasts through assimilation of observed streamflow for rainfall-dominated basins across the CONUS

Among different sources of uncertainty in hydrologic modeling (i.e., model structure, parameter estimation, input data, etc.), consecutive error reduction of model initial conditions can prevent a model from drifting away from reality and consequently improving model estimates. Most approaches that evaluated the correction of initial conditions through data assimilation (DA) have focused on improving hydrologic model simulations (i.e., under observed forcings) rather than evaluating the model performance in a forecasting context. This paper investigates the utility of Ensemble Kalman Filter (EnKF) data assimilation in which available observed streamflow is exploited to update state variables of a conceptual water balance model for forecasting monthly streamflow over 340 rainfall-dominated river basins across the contiguous United States (CONUS). Our results demonstrate that after EnKF application, streamflow simulation skill improves in terms of both Relative Root Mean Square Error (R-RMSE) and correlation coefficient (CC) for almost 90% of the selected river basins. Evaluating the model performance under different flow conditions shows that EnKF has stronger positive effect on monthly low flow predictions comparing to monthly high flows particularly during the summer season. The utility of EnKF is also assessed in the context of 1-month ahead streamflow forecasting. Due to the updated model initial conditions, streamflow forecasts are improved throughout the year even though the skill in hydrologic forecasts is predominantly dependent on the accuracy of precipitation forecasts.

The Impact of a New Set of IASI Channels on the Unified Model Global Precipitation Forecast

This study attempts to assess the impact of data assimilation with newly selected Infrared Atmospheric Sounding Interferometer (IASI) channels on the global precipitation forecast, using the Korea Meteorological Administration (KMA) Unified Model (UM) system. The new IASI channels assimilated under the clear-sky condition give a positive impact on the global precipitation forecast, shown in the trial experiments. In particular, the overestimated horizontal size of forecasted precipitation in the control run with operational IASI channels significantly decreases in the experimental trial with the newly selected channels. In addition, the moist biases of moisture field in the model analysis as an initial condition are substantially reduced in the experiment run. Considering that the moisture in the troposphere is a main source of precipitation, the reduction of moist biases in the troposphere seems to contribute to the improvement of precipitation forecast in the UM system. Therefore, these results suggest that the improved moisture field even over clear areas is able to enhance the precipitation forecast accuracy. And because the difference between two trial runs is only a set of IASI channels used in the UM data assimilation system, the improved precipitation forecast is likely to due to the use of different H2O channels in the new set of IASI channel used in the experiment run, which are sensitive to the tropospheric water vapor.

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

Detecting the Causal Effect of Soil Moisture on Precipitation Using Convergent Cross Mapping

As a vital land surface parameter, soil moisture influences climate through its impact on water and energy cycles. However, the effect of soil moisture on precipitation has been strongly debated. In this study, a new causal detection method, convergent cross mapping (CCM), was applied to explore the causality between soil moisture and precipitation over low- and mid- latitude regions in the Northern Hemisphere. CCM method generally identified a strong effect of soil moisture on precipitation. Specifically, the optimal effect of soil moisture on precipitation occurred with a lag of one month and clearly decreased after four months, suggesting that soil moisture has potentials to improve the accuracy of precipitation forecast at a sub-seasonal scale. In addition, as climate (i.e., aridity index) changed from dry to wet, the effect of soil moisture on precipitation first increased and then decreased with peaks in semi-arid and semi-humid areas. These findings statistically support the hypothesis that soil moisture impacts precipitation and also provide a reference for the design of climate prediction systems.

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1–5h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012–2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8μm and WV06.2μm brightness temperatures (BTs) with a horizontal resolution of 2.8km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.