Weather Research And Forecasting Forecasts Research Articles

Strategy analysis of the extrapolation adjusted by model prediction (ExAMP) blending scheme for rainfall nowcasting

The strategies of the extrapolation adjusted by model prediction (ExAMP) blending scheme, which trusts the field pattern predicted by extrapolation and allows the field intensity to be adjusted by numerical weather prediction (NWP), for rainfall nowcasting are analyzed in this study. The McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) and the Weather Research and Forecasting (WRF) model serve as the extrapolation and NWP models, respectively. Seven 150-min rainfall nowcasting experiments with different strategies are carried out for 37 sampled periods from seven heavy rainfall events in Taiwan in 2019. The results of the overall statistics indicate that, for the extrapolation component, extrapolating the current rainfall rate estimated from the lowest dual-polarimetric radar observations is a superior strategy. The ExAMP scheme that blends the MAPLE and WRF forecasts can surpass both components in 150-min rainfall nowcasting, and an empirical limitation on the innovation of intensity during the blending procedure is found unnecessary in this study. Moreover, the spatial performance for two contrasting events reveals the ability of ExAMP in grasping the rainfall strengthening and weakening in different areas. The skill statistics separately at rainfall strengthening gauges and weakening gauges further prove the effectiveness of ExAMP even though it is effective in intensity correction instead of pattern correction.

Exploring the Real-Time WRF Forecast Skill for Four Tropical Storms, Isaias, Henri, Elsa and Irene, as They Impacted the Northeast United States

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US.

Impacts of Assimilating FY-4A AGRI Clear-Sky Water Vapor Radiance on Short-Range Weather Prediction during Indian Summer Monsoon

ABSTRACT This study aims to assess the impacts of assimilating the clear-sky radiances from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite on 72-h forecasts. First, we compare the water vapour (WV) brightness temperature (TB) in July 2018 from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses and Weather Research and Forecasting (WRF) forecasts with the clear-sky AGRI WV channel observations. The results suggest that the NCEP GDAS analyses are more consistent with the AGRI observations than the WRF forecasts, and the AGRI observations can be utilized to improve the initial conditions of the WRF model by data assimilation, especially in water vapour and surface temperature. After the preliminary verification, two identical cycling assimilation experiments are performed with and without the AGRI WV TB assimilation for a month. The results reveal that the WRF forecasts with AGRI TB assimilation are closer to satellite observations than the first guess in both WV channels. The validation with WV channel data of the Microwave Humidity Sounder (MHS) and SAPHIR sensors indicates that after the AGRI data assimilation, the errors are smaller than in the control experiment (without AGRI assimilation). The assimilation of the TB observed by AGRI WV channels shows a remarkable positive impact on moisture forecasts at middle and upper levels. Comparison of the TB from the WRF forecasts with the MHS observations suggests that the AGRI WV channel assimilation can improve the predictions. Overall, assimilating AGRI WV observations can positively influence the WRF analyses and forecasts.

Improving the outputs of regional heavy rainfall forecasting models using an adaptive real-time approach

ABSTRACT In this work, an adaptive real-time approach is presented to improve the rainfall forecasts of the weather research and forecasting (WRF) model. Heavy rainfall events are considered to make 6-, 12-, 18-, and 24-hour ahead predictions, and to define various initial conditions of the forecasting model. Comparing various forecasts and observations specifies precise forecasting time line and physical settings of the WRF model that would lead to an improved forecasting model. An adaptive real-time approach is defined using a combination of observed precipitation and WRF forecasts. Forecasted precipitation values for each event are updated by the observed precipitation at each three-hour interval. A sigmoidal curve is fitted to the observations and remaining forecasts to provide modified values. The process continues step by step, and thus an adaptive real-time forecasting model is developed to improve predictions. The methodology is applied to Tehran and Golestan watersheds in Iran, and the results indicate that the developed methodology improves WRF forecasts in terms of categorical and statistical metrics.

Using an Artificial Neural Network to improve operational wind prediction in a small unresolved valley

AbstractThis study improves surface wind predictions in an unresolved valley using an artificial neural network (ANN). Forecasting winds in complex terrain with a mesoscale model is challenging. This study assesses the quality of 3-km wind forecasts by the Weather Research and Forecasting (WRF) model and the potential of post-processing by an ANN within the 1-2 km wide Cadarache Valley in southeast France. Operational wind forecasts for 110m above ground level and the near-surface vertical potential temperature gradient with a lead time of 24-48h were used as ANN input. Observed horizontal wind components at 10m within the valley were used as targets during ANN training. We use the Directional ACCuracy (DACC45, wind direction error ≤ 45°) and mean absolute error to evaluate the WRF direct model output and the ANN results. By post-processing, the score for DACC45 improves from 56% in the WRF direct model output to 79% after applying the ANN. Furthermore, the ANN performed well during the day and night, but poorly during the morning and afternoon transitions. The ANN improves the DACC45 at 10m even for poor WRF forecasts (direction bias ≥ 45°) from 42% to 72%. A shorter lead time and finer grid spacing (1 km) showed negligible impact which suggests that a 3 km grid spacing and a 24-48h lead time is effective and relatively cheap to apply. We find that WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. The ANN post-treatment consistently improves the wind forecast for all stability classes to a DACC45 of about 80%. The study demonstrates the ability to improve Cadarache valley wind forecasts using an ANN as post-processing for WRF daily forecasts.

Impact of Ensemble-Variational Data Assimilation in Heavy Rain Forecast over Brazilian Northeast

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are responsible for the rain episodes, and at the west side the northeastern hinterland and dry air masses are observed. This work is a case study that aims to evaluate the impact of updating the model initial condition using the 3DEnVar (Three-Dimensional Ensemble Variational) system in heavy rain episodes associated with Mesoscale Convective Systems (MCS). The results were compared to 3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square Root Filter) systems and with no data assimilation. The study enclosed two MCS cases occurring on 14 and 24 January 2017. For that purpose, the RMS (Regional Modeling System) version 3.0.0, maintained by the Center for Weather Forecasting and Climate Studies (CPTEC), used two components: the Weather Research and Forecasting (WRF) mesoscale model and the GSI (Gridpoint Statistical Interpolation) data assimilation system. Currently, the RMS provides the WRF initial conditions by using 3DVar data assimilation methodology. The 3DVar uses a climatological covariance matrix to minimize model errors. In this work, the 3DEnVar updates the RMS climatological covariance matrix through the forecast members based on the errors of the day. This work evaluated the improvements in the detection and estimation of 24 h accumulated precipitation in MCS events. The statistic index RMSE (Root Mean Square Error) showed that the hybrid data assimilation system (3DEnVar) performed better in reproducing the precipitation in the MCS occurred on 14 January 2017. On 24 January 2017, the EnSRF was the best system for improving the WRF forecast. In general, the BIAS showed that the WRF initialized with different initial conditions overestimated the 24 h accumulated precipitation. Therefore, the viability of using a hybrid system may depend on the hybrid algorithm that can modify the weights attributed to the EnSRF and 3DVar matrix in the GSI over the assimilation cycles.

Valley Winds at the Local Scale: Correcting Routine Weather Forecast Using Artificial Neural Networks

In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topography, are satisfactorily forecasted, and used as inputs to an artificial neural network (ANN) designed to reproduce wind observations inside the valley from WRF forecasts. A variable selection procedure identified 5 key input variables that best drive the ANN. With respect to the WRF output, the ANN significantly improves forecasted low-level winds, both for speed and direction. This study demonstrates the potential for the ANN technique to be used as a correcting tool to forecast weather conditions at the local scale when numerical modeling is performed at a resolution too coarse to take into account the effect of local topography.

Post-Processing of NWP Forecasts Using Kalman Filtering With Operational Constraints for Day-Ahead Solar Power Forecasting in Thailand

Solar power forecasting with a day-ahead horizon has played an important role in the operational planning of generating units in power system operations. We aim to develop a solar power forecasting model suitable for a tropical climate, using Thailand as a model, and hence present a linear recursive regression model as a post-processing step for reducing the errors obtained from the Weather Research and Forecasting (WRF) model. This model consists of submodels, each of which predicts the solar irradiance of a particular time of the day. By using a stepwise regression method, we found that WRF forecasts of irradiance, temperature, relative humidity, and the solar zenith angle were selected as highly relevant inputs of the model. The regression model coefficients are updated according to a Kalman filtering (KF) scheme so that the model can flexibly adapt to fluctuations in the solar irradiance. We then modify the KF update formula to accommodate the limitation in measurement availability at the time of executing the forecasts. The proposed KF formula can be generalized to find the optimal prediction given that the available measurements are mapped by an affine transformation. The obtained results using actual data from a solar rooftop system located in the central region of Thailand showed that the normalized root-mean-square error (NRMSE) of solar power prediction was about 12-13%, which was decreased from the NRMSE of the WRF model by 7-12% on average. This improvement was the best out of similar post-processing methods based on the model output statistics framework.

The Impacts of Soil Moisture Initialization on the Forecasts of Weather Research and Forecasting Model: A Case Study in Xinjiang, China

Soil moisture is a critical parameter in numerical weather prediction (NWP) models because it plays a fundamental role in the exchange of water and energy cycles between the atmosphere and the land surface through evaporation. To improve the forecast skills of the Weather Research and Forecasting (WRF) model in Xinjiang, China, this study investigated the impacts of soil moisture initialization on the WRF forecasts by performing a series of simulations. A group of simulations was conducted using the single-column model (SCM) from 1200 UTC on 15 to 18 August 2019, at Urumchi, Xinjiang (43.78° N, 87.6° E); another was performed using the WRF model for a real weather case in Xinjiang from 0000 UTC 15 August to 1200 UTC 18 August 2019, which included an episode of heavy precipitation and gales. Our most notable findings are as follows. Specific humidity increases and potential temperature decreases persistently when soil moisture increases because of soil water evaporation. Soil moisture initialization could impact the energy budget and modulate the partition of the total available energy at the land surface significantly through evaporation and the greenhouse effect. Replacing the soil moisture with a proper multiple of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) soil moisture data could significantly improve the critical success index (CSI) and frequency bias (FBIAS) of precipitation and the root-mean-squared errors (RMSEs) of 2-m specific humidity and 2-m temperature. These findings indicate the prospect of a new way to improve the forecast skills of WRF in Xinjiang or other similar regions.

Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco

Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.

Configuration of Statistical Postprocessing Techniques for Improved Low-Level Wind Speed Forecasts in West Texas

Abstract The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.

Influences of Horizontal Grid Spacing and Microphysics on WRF Forecasts of Convective Morphology Evolution for Nocturnal MCSs in Weakly Forced Environments

Abstract Nocturnal mesoscale convective systems (MCSs) are important phenomena because of their contributions to warm-season precipitation and association with severe hazards. Past studies have shown that their morphology remains poorly forecast in current convection-allowing models operating at 3–4-km horizontal grid spacing. A total of 10 MCS cases occurring in weakly forced environments were simulated using the Weather Research and Forecasting (WRF) Model at 3- and 1-km horizontal grid spacings to investigate the impact of increased resolution on forecasts of convective morphology and its evolution. These simulations were conducted using four microphysics schemes to account for additional sensitivities to the microphysical parameterization. The observed and corresponding simulated systems were manually classified into detailed cellular and linear modes, and the overall morphology depiction and the forecast accuracy of each model configuration were evaluated. In agreement with past studies, WRF was found to underpredict the occurrence of linear modes and overpredict cellular modes at 3-km horizontal grid spacing with all microphysics schemes tested. When grid spacing was reduced to 1 km, the proportion of linear systems increased. However, the increase was insufficient to match observations throughout the evolution of the systems, and the accuracy scores showed no statistically significant improvement. This suggests that the additional linear modes may have occurred in the wrong subtypes, wrong systems, and/or at the wrong times. Accuracy scores were also shown to decrease with forecast length, with the primary decrease in score generally occurring during upscale growth in the early nocturnal period.

A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

Development and Evaluation of an Ensemble-Based Data Assimilation System for Regional Reanalysis Over the Tibetan Plateau and Surrounding Regions.

The Tibetan Plateau is regarded as the Earth's Third Pole, which is the source region of several major rivers that impact more 20% the world population. This high‐altitude region is reported to have been undergoing much greater rate of weather changes under global warming, but the existing reanalysis products are inadequate for depicting the state of the atmosphere, particularly with regard to the amount of precipitation and its diurnal cycle. An ensemble Kalman filter (EnKF) data assimilation system based on the limited‐area Weather Research and Forecasting (WRF) model was evaluated for use in developing a regional reanalysis over the Tibetan Plateau and the surrounding regions. A 3‐month prototype reanalysis over the summer months (June−August) of 2015 using WRF‐EnKF at a 30‐km grid spacing to assimilate nonradiance observations from the Global Telecommunications System was developed and evaluated against independent sounding and satellite observations in comparison to the ERA‐Interim and fifth European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) global reanalysis. Results showed that both the posterior analysis and the subsequent 6‐ to 12‐hr WRF forecasts of the prototype regional reanalysis compared favorably with independent sounding observations, satellite‐based precipitation versus those from ERA‐Interim and ERA5 during the same period. In particular, the prototype regional reanalysis had clear advantages over the global reanalyses of ERA‐Interim and ERA5 in the analysis accuracy of atmospheric humidity, as well as in the subsequent downscale‐simulated precipitation intensity, spatial distribution, diurnal evolution, and extreme occurrence.

Evaluation of flood prediction capability of the distributed Grid‐Xinanjiang model driven by weather research and forecasting precipitation

AbstractThe lead time of operational flood forecasting is critical for the effectiveness of flood alert and flood risk reduction. It is impossible to extend the lead time of flood forecasting by solely using rain gauge observations. However, the weather research and forecasting (WRF) model has the potential to produce quantitative precipitation forecasts that can facilitate the flood risk management by increasing the flood forecasting lead time. This study investigates the flood prediction capabilities of the well‐tested Grid‐Xinanjiang model (GXM) in a flood‐prone area, located in the upper region of the Huaihe River Basin, when driven by gauge observations and WRF precipitation forecasts, respectively. The results indicate that GXM is capable of producing improved flood predictions by using the WRF precipitation forecasts. The incoming floods are difficult to be predicted in advance by using the gauge‐measured precipitation, especially when the lead time is larger than the flow concentration time. However, with the WRF forecasts, the occurrence of flood events can be predicted for longer lead times. This study also demonstrates that the temporal and spatial patterns of precipitation forecasts have an important impact on the prediction of both timing and magnitude of incoming floods.

Evaluation and Bias Correction in WRF Model Forecasting of Precipitation and Potential Evapotranspiration

Abstract A reliable forecast of potential evapotranspiration (ET0) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET0 and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET0 using the Penman–Monteith (PM) approach, as well as calculated ET0 and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM–ET0. Local Ws bias correction using observations on days preceding the forecast and interpolation of the resulting PM–ET0 to other locations led to significant improvement in ET0 forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET0 forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model–observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET0, the WRF Model was found to be a high-potential tool for improving crop irrigation management.

Seasonal Forecasting of Onset of Summer Rains over South Africa

AbstractIn this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.

Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction

Abstract This article presents the first practice of assimilating real-world all-sky GOES-16 ABI infrared brightness temperature (BT) observations using an ensemble-based data assimilation system coupled with the Weather Research and Forecasting (WRF) Model at a convection-allowing (1 km) horizontal resolution, focusing on the tornadic thunderstorm event across Wyoming and Nebraska on 12 June 2017. It is found that spurious clouds created before observed convection initiation are rapidly removed, and the analysis and forecasts of thunderstorms are significantly improved, when all-sky BT observations are assimilated with the adaptive observation error inflation (AOEI) and adaptive background error inflation (ABEI) techniques. Better forecasts of the timing and location of convection initiation can be achieved after only 30 min of assimilating BT observations; both deterministic and probabilistic WRF forecasts of midlevel mesocyclones and low-level vortices, started from the final analysis with 100 min of BT assimilation, closely coincide with the tornado reports. These improvements result not only from the effective suppression of spurious clouds, but also from the better estimations of hydrometeors owing to the frequent assimilation of all-sky BT observations that yield a more accurate analysis of the storm. Results show that BT observations generally have a greater impact on ice particles than liquid water species, which might provide guidance on how to better constrain modeled clouds using these spaceborne observations.

Impacts of Modifications to a Local Planetary Boundary Layer Scheme on Forecasts of the Great Plains Low-Level Jet Environment

AbstractThe Great Plains low-level jet (LLJ) is influential in the initiation and evolution of nocturnal convection through the northward advection of heat and moisture, as well as convergence in the region of the LLJ nose. However, accurate numerical model forecasts of LLJs remain a challenge, related to the performance of the planetary boundary layer (PBL) scheme in the stable boundary layer. Evaluated here using a series of LLJ cases from the Plains Elevated Convection at Night (PECAN) program are modifications to a commonly used local PBL scheme, Mellor–Yamada–Nakanishi–Niino (MYNN), available in the Weather Research and Forecasting (WRF) Model. WRF forecast mean absolute error (MAE) and bias are calculated relative to PECAN rawinsonde observations. The first MYNN modification invokes a new set of constants for the scheme closure equations that, in the vicinity of the LLJ, decreases forecast MAEs of wind speed, potential temperature, and specific humidity more than 19%. For comparison, the Yonsei University (YSU) scheme results in wind speed MAEs 22% lower but specific humidity MAEs 17% greater than in the original MYNN scheme. The second MYNN modification, which incorporates the effects of potential kinetic energy and uses a nonzero mixing length in stable conditions as dependent on bulk shear, reduces wind speed MAEs 66% for levels below the LLJ, but increases MAEs at higher levels. Finally, Rapid Refresh analyses, which are often used for forecast verification, are evaluated here and found to exhibit a relatively large average wind speed bias of 3 m s−1 in the region below the LLJ, but with relatively small potential temperature and specific humidity biases.

A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas

Abstract This work develops and tests the viability of obtaining skillful short-range (<20 h) visibility predictions using statistical postprocessing of a 4-km, 10-member Weather Research and Forecasting (WRF) ensemble configured to closely match the U.S. Air Force Mesoscale Ensemble Forecast System. The raw WRF predictions produce excessive forecasts of zero cloud water, which is simultaneously predicted by all ensemble members in 62% of observed fog cases, leading to zero ensemble dispersion and no skill in these cases. Adding dispersion to the clear cases by making upward adjustments to cloud water predictions from individual members not predicting fog on their own provides the best chance to increase the resolution and reliability of the ensemble. The technique leverages traits of a joint parameter space in the predictions and is generally most effective when the space is defined with a moisture parameter and a low-level stability parameter. Cross-validation shows that the method adds significant overnight skill to predictions in valley and coastal regions compared to the raw WRF forecasts, with modest skill increases after sunrise. Postprocessing does not improve the highly skillful raw WRF predictions at the mountain test sites. Since the framework addresses only systematic WRF deficiencies and identifies parameter pairs with a clear, non-site-specific physical mechanism of predictive power, it has geographical transferability with less need for recalibration or observational record compared to other statistical postprocessing approaches.