NWP Model Research Articles

Cyclogenesis in the lee of the Alps: a review of theories

Although the phenomenon has been known, and investigated, as early as the nineteenth century, the interest in understanding Alpine lee cyclogenesis (often called Genoa cyclogenesis) has grown since the middle twentieth century, when it was realized that the largest fraction of cyclones affecting the central-eastern Mediterranean and later Eastern Europe originated in the area south of the Alps, more often in the Gulf of Genoa. Forecasting this type of cyclogenesis remained a challenging task until at least the mid-late 1980s, even after the development of the earlier NWP models, which failed in predicting this phenomenon, lacking the ability to adequately represent the orographic forcing. Monitoring and understanding of cyclogenesis in the lee of the Alps was the main objective of field projects, the most important being GARP-ALPEX in 1982. The following years were full of ideas and theories about this phenomenon, which is representative of orographic cyclogenesis in other regions of the world. The main steps in understanding the complex phenomenon of lee cyclogenesis, with particular reference to the Alps, are outlined here, focusing on theoretical explanations.

The AEMET-<i>γ</i>SREPS over the Antarctic Peninsula and the impact of kilometric-resolution EPS on logistic activities on the continent

Abstract. Kilometric-resolution Ensemble Prediction Systems (EPSs) will be the new state-of-the-art forecasting tools for short-range prediction in the following decade. Their value will be even greater in Antarctica due to the increasingly demanding weather forecasts for logistic services. During the 2018–2019 austral summer (1 December–31 March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS was operationally integrated over the Antarctic Peninsula. In particular, the Antarctic version of γSREPS comes up with crossing four non-hydrostatic convection-permitting NWP models at 2.5 km with three global NWP driving models as boundary conditions. The γSREPS forecasting system has been validated in comparison with ECMWF EPS. It is concluded that γSREPS has an added value to ECMWF EPS due to both its higher resolution and its multi-boundary conditions and multi-NWP model approach. γSREPS performance has a positive impact on logistic activities at research stations and its design may contribute to polar prediction research.

Assimilation of GPS Zenith Total Delay estimates in RAMS NWP model: Impact studies over central Italy

The purpose of the data assimilation is to use all the available information to determine as accurately as possible the state of the atmosphere. In this context we consider the assimilation of the GPS-ZTD (Global Positioning System – Zenith Total Delay) by a nudging scheme in Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate ([email protected]) at medium horizontal resolution (5 km). The water vapour mixing ratio (qv) given by the [email protected] is perturbed according to the differences between observed and simulated ZTD. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over central Italy area, over a one-month time frame. The studied network consists of 41 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performance. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using goGPS, an Open Source Program Package for GNSS Positioning. For the application of PPP processing to single-frequency receivers, goSEID method (an alternative implementation of the SEID approach integrated in the goGPS open source software) was applied; the algorithm, starting from the observations of a unique (or more) dual frequency “reference” receiver, is able to generate L2 synthetic observations for any single frequency receiver located in its surroundings (15–20 km).

Dust induced radiative perturbations during an episode of long-range dust transport over Delhi, India: a high-resolution regional NWP model study

Dust-induced modifications to the radiative fluxes/heating during a light-blocking haze episode over Delhi, India, 7–9th Nov 2017—triggered by long range transport from a severe Saudi Arabian dust storm—have been investigated with a regional NWP model, NCUM-R with prognostic dust-radiation feedbacks. The study employs ‘Double Radiation Calls’, wherein parallel runs of the radiation scheme ‘with’ (prognostic) and ‘without’ (diagnostic) dust radiative effects—while prognostic fields drive the forecast—isolate the dust-induced perturbations. The forecasted dust optical depth agreed spatially with the AOD from MODIS with Angstrom Exponent > 0.5, indicating that the dust was well mixed with the fine mode anthropogenic aerosols upwind. The ‘downward shortwave (SW) flux’ was diminished (upto − 12.9 Wm−2) in layers sampling (i) near-surface (L1Avg), (ii) well-mixed layer within the planetary boundary layer (PBL, L2Avg) and (iii) free-troposphere (FT, L3Avg). Dust-induced ‘Solar heating’ dominated in FT (upto 9.5 × 10–7 Ks−1) and the patches below (in L2Avg) exhibited a cooling, leading to thermal dipoles. The ‘upward longwave (LW) flux’ in FT was reduced and ‘LW heating’ prevailed in all levels—peak (2.5 × 10–6 Ks−1) in L2Avg—along with well-defined cooling zones in L1Avg. The dust–radiation interaction in turn influenced the boundary layer meteorology, manifested as (i) shallow PBLs that spatially correlate with dust-induced cooling of the boundary layer column, (ii) enhanced surface humidity and (iii) reduced visibility. The study is an instance of prognostic dust-radiation feedbacks improving the skill of NWP models in dust-laden regions.

Simulation of a mesoscale convective system over Northern India: Sensitivity to convection partitioning in a regional NWP model

Simulations of a mesoscale convective system (MCS), which propagated across Northern India on 2nd May 2018 - leading to many fatalities when the gust front knocked down homes and tore apart building roofs - have been performed using the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model – Regional (4 km horizontal grid spacing), to evaluate the model’s convective treatments. Though the model captures many of the qualitative and quantitative features, it slightly lags behind the observed MCS organisation and movement, produces lesser precipitation, and lacks the spatial separation between two adjacent organised convective systems in the satellite observations – leading to a faintly offset MCS track. Sensitivity simulations are then performed, for this non-equilibrium MCS case, with different partitioning between parametrized and explicit convection to assess the reliance of the convective treatments on the large-scale environment, as well as to test the notion of a breakdown of convective parametrization at the mesoscale model resolution. Fully parametrized (FP) convection produces even lesser rainfall and are dominated by orographic precipitations along the foot hills of Himalayas with no any trace of the MCS. Fully explicit (FE) convection realistically simulates most of the prominent convective cells and enhance precipitation along the MCS track that agree better with the observations, though the ‘two lobes’ of intense precipitation are not resolved; instead it produces a squall line of precipitation. The FE configuration generates the most vigorous convective updraft, along with a vertical shear that is tilted westward. The simulation with partially parametrized and partially explicit convection resembles the fashion in the FP and FE scenarios, with a transition over the duration of the run from parametrized to explicit precipitation. The results are in line with the notion from previous studies; that the majority of successful explicit simulations of mesoscale organisation are those associated with strong large-scale forcing for convection, wherein resolved vertical motions are sufficient to minimise delays in onset.

Statewide real-time quantitative precipitation estimation using weather radar and NWP model analysis: Algorithm description and product evaluation

This study describes an automated system that generates a statewide real-time quantitative precipitation estimation (QPE) product for flood forecasting in Iowa. The QPE system comprises, real-time data acquisition, processing, and product visualization subsystems. Combined with information retrieved from numerical weather prediction, the system processes data from multiple radars using various algorithms accounting for precipitation microphysics and radar remote sensing uncertainties. The system generates a composite rainfall map covering the entire state of Iowa at a resolution of 0.5 km, updated every 5 min. With the help of the system's flexible modular configuration, we have recently added a new polarimetric algorithm based on specific attenuation. Independent evaluations based on comparisons with rain gauge data and hydrologic model prediction of streamflow demonstrate that the new implementation significantly improves the rainfall estimation accuracy. The new QPE product shows performance comparable to the Multi-Radar Multi-Sensor product that contains a rain gauge correction.

Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France

Abstract. As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6.

Comparing solar data from NWP models for Brazilian territory

Brazil is experiencing continuous growth in the solar energy market over the last years, and it is expected that solar power plants will have a significant contribution to the Brazilian energy matrix soon. The solar irradiance variability is an important issue due to its impact on electricity distribution systems. However, solar energy variability has not been comprehensively evaluated in Brazil. The study aimed to assess the capacity of two numerical models of mesoscale in simulating the solar radiation conditions in the Northeastern region of Brazil. For this, a case study was conducted comparing the surface solar irradiation data acquired in 41 automated weather stations operating in the study area and managed by the Brazilian Institute of Meteorology with estimates provided by two weather mesoscale models BRAMS and WRF. The study indicated that the difference between the model's uncertainties is small (typically less than 5%) when evaluating the entire region of the Brazilian Northeast. However, local comparisons may show more notable discrepancies reflecting uncertainties in the estimates produced by the models.

The potential use of operational radar network data to evaluate the representation of convective storms in NWP models

AbstractOperational forecasting centres increasingly rely on convection‐permitting NWP simulations to assist in their forecasting of convective events. The evaluation of upgrades in the underlying NWP modelling system normally happens through routine verification using traditional metrics on two‐dimensional fields, such as gridded rainfall data. Object‐ and process‐based evaluation can identify specific physical mechanisms for model improvement, but such evaluation procedures normally require targeted and expensive field campaigns. Here, we explore the potential use of the UK operational radar network observations and its derived 3D composite product for evaluating the representation of convective storms in the Met Office Unified Model. A comparison of the 1 × 1 × 0.5 km 3D radar composites against observations made with the research‐grade radar at Chilbolton in the southern UK indicates that the 3D radar composite data can reliably be used to evaluate the morphology of convective storms. The 3D radar composite data are subsequently used to evaluate the development of convective storms in the Met Office Unified Model. Such analysis was previously unavailable due to a lack of 3D radar data of high temporal frequency. The operational nature of the UK radar data makes these 3D composites a valuable resource for future studies of the initiation, growth, development, and organisation of convective storms over the UK.

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Low-visibility conditions are a weather hazard that affects all forms of transport, and accurate forecasting of their spatial coverage is still a challenge for meteorologists, particularly over a large domain. Current predictions of visibility are based on physical parametrizations in mesoscale models and are thus limited with respect to accuracy. This paper examines the use of supervised machine-learning regression techniques (tree-based ensemble, feed-forward neural network and generalized linear methods) to diagnose visibility from operational mesoscale model forecasts over a large domain. To achieve this, hourly forecasts of meteorological parameters in the lower levels of the atmosphere have been used. In the short-range forecasting framework, the machine-learning algorithms were developed to provide hourly forecasts up to 24 h. To assess the performance of the developed models, hourly observed data, collected at 36 synoptic land stations over the northern part of Morocco, have been used. This region is characterized by a heterogeneous topography. The tree-based ensemble methods have shown some improvement in visibility forecasting in comparison with the operational visibility diagnostic scheme based on Kunkel’s formula and also with persistence. It is also found that this machine-learning technique performs better when the forecast depends on multiple predictors instead of only a few with very high importance. In addition, their performance is very sensitive to the disproportionality of data availability between daytime and night-time. Furthermore, it is found that the performance decreases when principal components are used instead of raw correlated data.

Characterizing NWP Model Errors Using Doppler-Lidar Measurements of Recurrent Regional Diurnal Flows: Marine-Air Intrusions into the Columbia River Basin

Abstract Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

CASA Prediction System over Dallas–Fort Worth Urban Network: Blending of Nowcasting and High-Resolution Numerical Weather Prediction Model

AbstractThis study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.

Studying the Intensity and Storm Surge Phenomena of Tropical Cyclone Roanu (2016) over the Bay of Bengal Using NWP Model

Tropical Cyclone (TC) is the most devastating atmospheric incidents which occur frequently in pre-monsoon and the post-monsoon season in Bangladesh. The Bay of Bengal (BoB) is one of the most vulnerable places of TC induced storm surge. The triangular shape of BoB plays an important role to drive the sea water towards the coast and amplify the surges. In this study, minimum central pressure, maximum wind speed and track of TC Roanu are predicted by the WRF model. At the same time, prediction of cyclone induced storm surge for TC Roanu is done by using MRI storm surge model which is conducted by JMA. The input files for this parametric model is provided by using simulated data of WRF model and observed data of IMD. The results are compared with available recorded data of surge height for this cyclone. The differences in simulated output for two different input files are also studied. The maximum surge height from the MRI model is found 3 m using WRF simulated data and for IMD estimated data the maximum surge height is found 2.5 m. The simulated surge heights are found in decent contract with the available reported data of the storm surges.

GEOVISUAL ANALYTICS ON THE VERIFICATION OF THE PAGASA OPERATIONAL NUMERICAL WEATHER PREDICTION MODEL RAINFALL FORECAST

Abstract. Assessment of NWP model performance is an integral part of operational forecasting as well as in research and development. Understanding the bias propagation of an NWP model and how it propagates across space can provide more insight in determining underlying causes and weaknesses not easily determined in traditional methods. The study aims to introduce the integration of the spatial distribution of error in interpreting model verification results by assessing how well the operational numerical weather prediction system of PAGASA captures the country’s weather pattern in each of its climate type. It also discusses improvements in model performance throughout the time-frame of analysis. Error propagation patterns were identified using Geovisual Analytics to allow comparison of verification scores among individual stations. The study concluded that a major update in the physics parameterization of the model in 2016 and continued minor updates in the following years, surface precipitation forecasts greatly improved from an average RMSE of 9.3, MAE of 3.2 and Bias of 1.36 in 2015 to an RMSE of 7.9, MAE of 2.5 and bias of −0.63 in 2018.

WMO Typhoon Landfall Forecast Demonstration Project (WMO-TLFDP) Progress and Future Plans

ABSTRACT The “WMO Typhoon Landfall Forecast Demonstration Project (TLFDP)” was started in May 2010 in conjunction with the start of the Shanghai World Expo 2010. The project was successful in terms of assisting the local forecasters in providing an efficient tropical cyclone (TC) forecast service for World Expo 2010, demonstrating the performance of the most up-to-date techniques in TC forecasting, and enhancing the ability of forecasters to effectively use products based on advanced TC forecasting techniques. The first phase was completed in December 2012 and the second phase was completed in December 2015. The third phase (TLFDP-III) started in 2016. During its first (2010-2012) and second (2013-2015) phases, TLFDP collected real-time TC forecast products from 15 Typhoon Forecast Product Providers (TFPPs). The products include deterministic track and intensity forecasts, ensemble track and intensity forecasts, deterministic wind radii forecasts, wind probability forecasts, and gridded model outputs. The products were disseminated through the project’s website and the operational website of the Shanghai Typhoon Warning Center. TLFDP has made significant progress in TC forecast verification, including setting up the tools for both realtime and post-season TC forecast verification, developing and integrating several new verification techniques, carrying out a survey on the operational status of TC forecast verification in the western North Pacific region, and contributing to the WMO document “Verification of tropical cyclone forecasts”. The post-season forecast verification was reported to the Session of the UNESCAP/WMO Typhoon Committee since 2013. New consensus methods for TC track and intensity forecasts were also proposed either based on Ensemble Prediction Systems (EPSs) products or multiple deterministic NWP model products. Two training workshops on TC forecasting were organized by the TLFDP and held in Shanghai, China, in May 2010 and June 2012, respectively, and seven research fellowship projects were implemented as jointly supported by the UNESCAP/WMO Typhoon Committee, Shanghai Typhoon Institute and Hong Kong Observatory. In its third phase (TLFDP-III, 2016-2018), the project will continue its effort on demonstration and evaluation of the newly developed forecasting techniques for TCs, and developing and documenting advanced guidance for the verification of TC forecasts, with special attention to TC intensity and precipitation.

A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment

The ability of four models to provide short-term (up to 6 h ahead) GHI and DNI forecasts in the Iberian Peninsula is assessed based on two years of data collected at four stations. The models follow (mostly) independent approaches: one pure statistical model (Smart Persistence), one model based on CMV derived from satellite images (Satellite), one NWP model (WRF-Solar) and a hybrid satellite-NWP model (CIADCast). Overall, results show Smart Persistence to be the best at the first lead steps, advective models (Satellite and CIADCast) at intermediate ones and the WRF-Solar at the end of the forecasting period. The break-even point between the advective models and WRF-Solar varies between 1 and 3 h for GHI and 3 and 5 h for DNI. Nevertheless, a detailed analysis shows enormous differences between models performance related to 1) the local geographic and topographic conditions of the evaluation stations; 2) the evaluated variable (GHI vs. DNI); and 3) the sky and synoptic weather conditions over the study area. Depending on the station and lead time, rRMSE values range from 25% to 70% for GHI and from 35% to 100% for DNI. For the same stations and leading time, rRMSE values for DNI are between 50% and 100% higher than the corresponding GHI counterparts. Depending on the synoptic pattern, rRMSE values are about 10/20% for GHI/DNI (3 h lead time, during high pressure conditions) to about 80/180% for GHI/DNI (during low pressure conditions). All models show a poor performance at a coastal station, attributed to a lack of ability to forecast clouds associated with sea-land breezes. To conclude, no single model proves to be the best performing model and, therefore, results show that the four models are, somehow, complementary. The advantages attained by this complementarity are further explored in a companion paper (Part II).

Validating INSAT-3D atmospheric temperature retrievals over india using radiosonde measurements and other satellite observations

Atmospheric temperature profiles retrieved from a geostationary INSAT-3D (Indian National Satellite System) sounder level-2 physical retrieval data are evaluated with respect to radiosonde observations, Atmospheric Infrared Sounder (AIRS), and National Centers for Environmental Prediction and Department of Energy (NCEP-DOE) reanalysis-2 data sets over India and adjoining area. This evolution was carried out in the terms of correlation coefficients, bias, and root-mean-square error (RMSE) at each pressure level from surface 1000 to 100 hPa during a one year period in 2016.The initial validation of INSAT-3D temperature profiles with AIRS and NCEP-DOE reanalysis-2 data were taken as reference and computed for the spatial distribution of correlation coefficient, bias, and RMSE at each pressure level over the Indian region. In addition to that, we also compared the INSAT-3D temperature retrievals with 12 selected IMD (India Meteorological Department) radiosonde observations at different geographical features over the Indian subcontinent during the one year period in 2016. In this analysis, for each station at all pressure levels, the correlation coefficients, biases and their corresponding root-mean-square errors (RMSEs) were carried out between INSAT-3D and radiosonde observations. The results of the inter-comparison reveal that temperature retrievals from the INSAT-3D are a good agreement with AIRS and NCEP-DOE reanalysis-2 over the land region and a slight degradation performance over the ocean- and coastal regions of Arabian Sea and Bay of Bengal below 850 hPa. The degradation performance over the ocean- and coastal areas associated with maybe possibility of undetected clouds and uncertainty in surface emissivity and also it might be attributed to improper bias correction coefficients used for brightness temperature of clear-sky pixels before physical retrievals in INSAT-3D algorithm or inherent deficiency in the retrieval algorithm which mainly depends on initial guess field from NWP models. Furthermore, an evaluation of INSAT-3D with 12 IMD radiosonde observation results reveals that temperature retrievals for each station shows very good agreement at all pressure levels, except slight degradation in performance over the coastal regions (GOA, MDS) and island (AMN, PBL) stations possibly the same reason as that of AIRS/NCEP. Overall, the results show that INSAT-3D temperature retrievals over land are a good agreement with AIRS/NCEP and RSRW then over the ocean- and coastal regions which need to be analyzed.

Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics

As the penetration of photovoltaic power in the electrical grid increases, reliable forecasts of solar irradiance are becoming ever more critical. For forecasting horizons beyond a few hours, Numerical Weather Prediction models (NWP) are stapled to yield the best results. For potential users of NWP forecasts, choosing which model to use can be challenging, as a large palette of NWP models and configurations is available. To tackle this issue, several studies have conducted systematic comparisons between available models and configurations. Most of these studies, however, only evaluate forecast skills with a small number of metrics, such as the Root Mean Square Error (RMSE) or the Mean Average Error (MAE). To better understand the nature and specificities of various NWP models, we developed an evaluation approach that combines multiple-criteria analysis, Fourier analysis and classification of daily profiles of irradiance. In this paper, we present this novel approach and demonstrate its strengths by applying it to a practical use-case, using 108 different NWP forecasts models based on different parametrizations of the mesoscale NWP Weather Research and Forecasting model (WRF) in Singapore. Our approach allowed a clearer overview of the forecasts’ skills, as well as better discrimination between models. It also significantly improved our understanding of the nature of the NWP errors. In particular, WRF forecasts in Singapore were found unfit to timely resolve irradiance at an hourly scale, but better adapted to predict daily profiles of irradiance. Furthermore, the proposed multiple criteria approach was applied to a sensitivity analysis of WRF physical schemes, providing a better insight into the impact of each model. This work shows that it is critical to go beyond RMSE when evaluating forecasts and that more holistic approaches such as the one proposed here should be preferred.

Probabilistic streamflow forecast based on spatial post-processing of TIGGE precipitation forecasts

Ensemble precipitation forecast is effective in reducing the uncertainty and providing reliable probabilistic streamflow forecast. However, for operational applications, precipitation forecasts must go through bias correction in mean and spread. Although post-processing methods, such as BMA, have demonstrated good performance in ensemble-based calibration, the spatial correlation between stations may be altered after post-processing. In this research, ensemble precipitation forecasts of four NWP models, including ECMWF, UKMO, NCEP, and CMA within the TIGGE database, was bias-corrected and post-processed using quantile mapping and BMA for a case study basin in Iran. The ECC method was then used to recover the spatial correlation of ensemble forecasts. Subsequently, probabilistic streamflow forecast was conducted using post-processed precipitation forecasts. The results showed that the errors in the mean and spread of ensemble precipitation forecasts were corrected for each of the four NWP models while the ECC method was effective in maintaining spatial correlation. Furthermore, the results of probabilistic streamflow forecast showed that the performance of the forecast models improved after post processing, with the ECMWF model providing the best forecasts. More work is recommended to improve the impact of the ECC method on NWP models’ performance.

Improving RAMS and WRF mesoscale forecasts over two distinct vegetation covers using an appropriate thermal roughness length parameterization

Land Surface Models (LSM) have shown some difficulties to properly simulate day-time 2-m air and surface skin temperatures. This kind of models are coupled to atmospheric models in mesoscale modelling, such as the Regional Atmospheric Modeling System (RAMS) and the Weather Research and Forecasting (WRF) Model. This model coupling is used within Numerical Weather Prediction Systems (NWP) in order to forecast key physical processes for agricultural meteorology and forestry as well as in ecological modelling. The current study first evaluates the surface energy fluxes and temperatures simulated by these two state-of-the-art NWP models over two distinct vegetated covers, one corresponding to a poor and sparsely vegetated area and the other one corresponding to the tall and well-vegetated area of a forest. On the other hand, the importance of parameterizing the thermal roughness length within the LSM coupled to the corresponding atmospheric model is also evaluated. The LEAF-3 LSM is used within the RAMS modelling environment while the Noah-MP LSM is applied within WRF. Results indicate that the original version of the models underestimates the temperature during the day, more remarkably in the forested area, whereas modifications in the thermal roughness length successfully simulates the temperature and sensible heat flux forecasts over this area. This study highlights the key role of the surface exchange processes when coupling land and atmosphere models. In this regard, incorporating an extra resistance in the surface-layer parameterization through the thermal roughness length is essential to simulate well both temperatures and sensible heat fluxes, which becomes more relevant over tall and well-vegetated areas, such as a forest. This extra resistance for heat exchange prevents effective molecular diffusion in the layer between the momentum roughness length and the thermal roughness length. Additionally, an appropriate description of the canopy height permits to apply an improved surface-layer formulation over different land and vegetation covers.