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Diabatic forcing and initialization with assimilation of cloud and rain water in a forecast model: Methodology

The focus of this part of the investigation is to find one or more general modeling techniques that will help reduce the time taken by numerical forecast models to initiate or spin-up precipitation processes and enhance storm intensity. If the conventional data base could explain the atmospheric mesoscale flow in detail, then much of our problem would be eliminated. But the data base is primarily synoptic scale, requiring that a solution must be sought either in nonconventional data, in methods to initialize mesoscale circulations, or in ways of retaining between forecasts the model generated mesoscale dynamics and precipitation fields. All three methods are investigated. The initialization and assimilation of explicit cloud and rainwater quantities computed from conservation equations in a mesoscale regional model are examined. The physical processes include condensation, evaporation, autoconversion, accretion, and the removal of rainwater by fallout. The question of how to initialize the explicit liquid water calculations in numerical models and how to retain information about precipitation processes during the 4-D assimilation cycle are important issues that are addressed. The explicit cloud calculations were purposely kept simple so that different initialization techniques can be easily and economically tested. Precipitation spin-up processes associated with three different types of weather phenomena are examined. Our findings show that diabatic initialization, or diabatic initialization in combination with a new diabatic forcing procedure, work effectively to enhance the spin-up of precipitation in a mesoscale numerical weather prediction forecast. Also, the retention of cloud and rain water during the analysis phase of the 4-D data assimilation procedure is shown to be valuable. Without detailed observations, the vertical placement of the diabatic heating remains a critical problem.

Raymond, William H.↗

The Importance of Convective Stage on Out-of-Cloud Convectively Induced Turbulence from High-Resolution Simulations

Turbulence (clear-air, mountain wave, convectively induced) is an aviation hazard that is a challenge to forecast due to the coarse resolution ultilized in operational weather models. Turbulence indices are commonly used to aid pilots in avoiding turbulence, but these indices have been designed and calibrated for midlatitude clear-air turbulence prediction (e.g., the Ellrod index). A significant limitation with current convectively induced turbulence (CIT) prediction is the lack of storm stage dependency. In this study, six high-resolution simulations of tropical oceanic and midlatitude continental convection are performed to characterize the turbulent environment near various convective types during the developing and mature stages. Second-order structure functions, a diagnostic commonly used to identify turbulence in turbulence prediction systems, are used to characterize the probability of turbulence for various convective types. Turbulence likelihood was found to be independent of region (i.e., tropical vs midlatitude) but dependent on convective stage. The probability of turbulence increased near developing convection for the majority of cases. Additional analysis of static stability and vertical wind shear, indicators of turbulence potential, showed that the convective environment near developing convection was more favorable for turbulence production than mature convection. Near developing convection, static stability decreased and vertical wind shear increased. Vertical wind shear near mature and developing convection was found to be weakly correlated to turbulence intensity in both the tropics and the midlatitudes. Finally, this study emphasizes the need for turbulence avoidance guidelines for the aviation community that are dependent on convective stage.

54 ENVIRONMENTAL SCIENCES↗

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

numerical weather forecasting↗

Robustness and Behavior of Adjoint Calculations of Observation Impacts in Numerical Weather Prediction

Adjoint models are powerful tools that can be used to estimate the impact of observations on a chosen norm for numerical weather prediction forecasts. In this study, the Global Modeling and Assimilation Office (NASA/GMAO) Observing System Simulation Experiment framework is employed to investigate the behavior of the adjoint tool in an environment where the 'true' state of the atmosphere is fully known. This allows for the calculation of adjoint estimates of observation impact for very short forecast times including the zero-hour analysis state. The adjoint calculations using self-analysis verification can also be compared to adjoint calculations using the 'truth' as verification in order to characterize the robustness of adjoint estimations in the operational setting. Results from a experiments exploring various aspects of performance of the adjoint tool will be presented.

Prive, Nikki C.↗

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗

Wind ramp events validation in NWP forecast models during the second Wind Forecast Improvement Project (WFIP2) using the Ramp Tool and Metric (RT&M)

The second Wind Forecast Improvement Project (WFIP2) is a multi-agency field campaign held in the Columbia Gorge area (October 2015 - March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well-known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011 - August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and 3 lidars, and to forecasts from the High Resolution Rapid Refresh (HRRR), 3-km, and from the High Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

Djalalova, Irina V.↗

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗

The impact of Seasat-A scatterometer data on the numerical prediction of the Queen Elizabeth II storm

The development of the Queen Elizabeth II storm from 1200 UT on September 9, 1978, to 0000 UT on September 11, 1978, is forecast with a limited-area, fine resolution (100 km) numerical weather prediction model. Forecasts from initial conditions that include and exclude Seasat scatterometer surface winds are performed. When the Seasat winds are allowed to influence the upper levels of the atmosphere, a large positive impact is found.

Duffy, D. G.↗

Evaluating the WFIP2 updates to the HRRR model using scanning Doppler lidar measurements in the complex terrain of the Columbia River Basin

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

17 WIND ENERGY↗

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗

5-minute Wind Power Data based on WFIP2 WRF Simulation

The second Wind Forecast Improvement Project (WFIP2) was a public-private partnership funded by the U.S. Department of Energy and NOAA, aimed at enhancing the forecast skill of numerical weather prediction models for turbine-height winds in regions with complex terrain. An 18-month Weather Research and Forecasting (WRF) model simulation was conducted over the Pacific Northwest, with model outputs validated against observational data collected during WFIP2. Simulated wind speeds were used to estimate wind power generation using reV (the Renewable Energy Potential Model developed by NREL) at ten wind project sites. Two sets of results were produced: one using wind speeds extracted from the model grid cell at the project centroid, and another using wind speeds from the actual turbine locations. For each dataset, power output was calculated using both actual turbine-specific power curves and nine generic power curves to convert wind speed into power.

17 WIND ENERGY↗

Enhancing Biomass Productivity by Forecast‐Informed Pond Operations

ABSTRACT Microalgal cultivation for biofuels and proteins holds significant promise but faces challenges in achieving economically viable biomass productivity under variable environmental conditions. This study introduces a forecast‐informed pond operation (FIPO) system that uses numerical weather prediction (NWP) ensemble forecasts and the biomass assessment tool (BAT) to optimize daily dilution rates for enhanced biomass production. In contrast to the current practice, where fixed dilution rates are based on operator experience, the FIPO system determines the optimal dilution rate based on future weather forecasts and biomass growth conditions. Our experiments validate the effectiveness of FIPO in both short‐ and long‐term growth scenarios. In short‐term experiments, FIPO increased biomass production by 21.3% compared to batch growth and 7.4% over fixed dilution (60% every 3 days) operations. The NWP forecast‐informed operations achieved biomass production nearly identical to that using perfect weather forecasts, highlighting the accuracy of current NWP forecasts for guiding pond operations. In long‐term experiments, FIPO resulted in biomass production increases of 13.3% and 17.8% compared to two fixed dilution rates (60% every 3 days and 20% daily). These findings underscore the viability of using NWP forecasts to optimize microalgal cultivation systems. By adjusting daily dilution rates in response to forecasted weather, operators can achieve higher biomass yields and mitigate risks associated with environmental variability. This study provides a foundation for future research and practical applications in commercial‐scale microalgal production.

Yan, Hongxiang [Energy and Environment Directorate↗

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud‐computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high‐performance computing environments, supporting multi‐node systems required for near real‐time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on‐demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on‐demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre‐compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF‐EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher‐resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short‐term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity‐building applications that extend numerical weather prediction to developing countries are intended to provide near real‐time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather‐related hazards, or impacts that local weather events may have during the recovery phase.

Molthan, Andrew↗

Initial weather regimes as predictors of numerical 30-day mean forecast accuracy

Thirty 30-day mean 500-mb-height anomaly forecasts generated by the NCAR Community Climate Model (CCM) for the year 1978 are examined in order to determine if the forecast accuracy can be estimated with the initial conditions. The initial weather regimes were defined in such a way that the regimes could discriminate between the best and the worst 30-day mean forecasts run from the initial fields in this data set. On the basis of the CCM experiments, it is suggested that the accuracy of numerical 30-day mean forecasts may depend upon the accuracy with which the cyclones and their interactions with the planetary scale are predicted early in the forecast cycle, and that this accuracy may depend upon the initial conditions.

Colucci, Stephen J.↗

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

Cloud computing capabilities have rapidly expanded within the private sector, offering new opportunities for meteorological applications. Collaborations between NASA Marshall, NASA Ames, and contractor partners led to evaluations of private (NASA) and public (Amazon) resources for executing short-term NWP systems. Activities helped the Marshall team further understand cloud capabilities, and benchmark use of cloud resources for NWP and other applications

Disasters↗