Numerical Weather Prediction System Research Articles

SWING, The Score-Weighted Improved NowcastinG Algorithm: Description and Application

Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field simulation. The overarching goal is to pave the way for an easy and automatic communication of the heavy rainfall warning derived by the nowcasting procedure. The results obtained applying the SWING algorithm over a case study of 22 days in the fall 2019 season suggest that the algorithm could improve the predictive capability of a traditional deterministic nowcasting forecast system, keeping a useful forecast timing and thus integrating the current forecast procedures. Eventually, the main advantage of the SWING algorithm is also its very high versatility, since it could be used with any meteorological model also in a multi-model forecast approach.

On the Importance of Representing Snow Over Sea‐Ice for Simulating the Arctic Boundary Layer

AbstractCorrectly representing the snow on sea‐ice has great potential to improve cryosphere‐atmosphere coupling in forecasting and monitoring (e.g., reanalysis) applications, via improved modeling of surface temperature, albedo and emissivity. This can also enhance the all‐weather all‐surface coupled data assimilation for atmospheric satellite radiances. Using wintertime observations from two Arctic field campaigns, SHEBA and N‐ICE2015, and satellite data, we explore the merits of different approaches to represent the snow over sea‐ice in a set of 5‐day coupled forecasts. Results show that representing the snow insulation effects is essential for capturing the wintertime surface temperature variability over sea‐ice and its response to changes in the atmospheric forcing. Modeling the snow over sea‐ice improves the representation of strong cooling events, reduces surface temperature biases in clear‐sky conditions and improves the simulation of surface‐based temperature inversions. In clear‐sky conditions, when using a multi‐layer snow scheme the root‐mean‐squared error in the surface temperature is reduced by about 60% for both N‐ICE2015 and SHEBA. This study also highlights the role of compensating errors in different components of the surface energy budget in the Arctic boundary layer. During warm air intrusions, errors in the surface temperature increase when cloud phase and cloud radiative processes are misrepresented in the model, inducing large errors in the net radiative energy at the surface. This work indicates that numerical weather prediction systems can fully benefit from a better representation of snow over sea‐ice, for example, with multi‐layer snow schemes, combined with improvements to other boundary layer processes including mixed phase clouds.

ACCESS-C: Australian Convective-Scale NWP with Hourly 4D-Var Data Assimilation

Abstract The Australian Bureau of Meteorology recently upgraded its convection-allowing numerical weather prediction system, known as the Australian Community Climate and Earth System Simulator (ACCESS-C). ACCESS-C includes seven domains covering major population centers, nested inside the Bureau’s global NWP system. The upgrade included the introduction of data assimilation, with hourly cycling 4D-Var. With a much newer version of the Unified Model to provide the forecast, a range of storm attribute diagnostics to improve forecasting of severe weather events could be introduced. This paper details the configuration of the new version of ACCESS-C. Some verification compared with its predecessor (a downscaling system of comparable resolution) is presented. Of greater note is an exploration of the differences in the model characteristics between the new and old systems, which will affect how users interpret the outputs.

Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA's global forecast system

AbstractThe European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models

Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction (NWP) in non-Gaussian setting. In this manuscript, two recent versions of particle filters, namely the Localized Adaptive Particle Filter (LAPF) and the Localized Mixture Coefficient Particle Filter (LMCPF) are studied in comparison with the Ensemble Kalman Filter when applied to the popular Lorenz 1963 and 1996 models. As these particle filters showed mixed results in the global NWP system at the German meteorological service (DWD), the goal of this work is to show that the LMCPF is able to outperform the LETKF within an experimental design reflecting a standard NWP setup and standard NWP scores. We focus on the root-mean-square-error (RMSE) of truth minus background, respectively, analysis ensemble mean to measure the filter performance. To simulate a standard NWP setup, the methods are studied in the realistic situation where the numerical model is different from the true model or the nature run, respectively. In this study, an improved version of the LMCPF with exact Gaussian mixture particle weights instead of approximate weights is derived and used for the comparison to the Localized Ensemble Transform Kalman Filter (LETKF). The advantages of the LMCPF with exact weights are discovered and the two versions are compared. As in complex NWP systems the individual steps of data assimilation methods are overlaid by a multitude of other processes, the ingredients of the LMCPF are illustrated in a single assimilation step with respect to the three-dimensional Lorenz 1963 model.

Summertime Assessment of an Urban-Scale Numerical Weather Prediction System for Toronto

Urban-scale Numerical Weather Prediction (NWP) systems will be important tools for decision-making in and around large cities in a changing climate exposed to more extreme weather events. Such a state-of-the-art real-time system down to 250-m grid spacing was implemented in the context of the Toronto 2015 Panamerican games, Canada (PanAm). Combined with the Global Environmental Multiscale (GEM) model, attention was brought to the representation of the detailed urban landscape, and to the inclusion of sub-daily variation of the Great Lakes surface temperature. Results show a refined representation of the urban coastal environment micro-meteorology with a strong anisotropy of the urban heat island reaching about 2 °C on average for the summer season, coastal upwelling, and mesoscale features such as cumulus clouds and lake-breeze flow. Objective evaluation at the surface with a dense observational network reveals an overall good performance of the system and a clear improvement in comparison to reference forecasts at 2.5-km grid spacing in particular for standard deviation errors in urban areas up to 0.3 °C for temperature and dew point temperature, and up to 0.5 m s−1 for the wind speed, as well as for precipitation with an increased Equitable Threat Score (ETS) by up to 0.3 for the evening accumulation. The study provides confidence in the capacity of the new system to improve weather forecasts to be delivered to urban dwellers although further investigation of the initialization methods in urban areas is needed.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Abstract. Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

AbstractConvective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.

Land–Snow Data Assimilation Including a Moderately Coupled Initialization Method Applied to NWP

Abstract Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer. Significance Statement Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.

Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

AbstractWe present the first ever real‐time numerical weather prediction system with 30‐s update cycles at a 500‐m grid spacing for the prediction of convective precipitation in the subsequent 30 min using a new‐generation multi‐parameter phased array weather radar. The system comprises a regional atmospheric model known as the SCALE and the local ensemble transform Kalman filter (LETKF). To accelerate the SCALE‐LETKF system, data transfer between the two aforementioned components is performed using a memory copy instead of a file I/O. A complete real‐time workflow including domain nesting and observational data transfer is constructed. A real‐time test in July and August 2020 showed that the system is fast enough for a real‐time application of 30‐s forecast‐analysis cycles and 30‐min prediction. The development includes a new thinning method considering the spatially correlated observation errors in the dense radar data. This new thinning method is effective in two past case studies in the summer of 2019.

Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones

Abstract. ECOCLIMAP-Second Generation (ECO-SG) is the land-cover map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM Numerical Weather Prediction system used for short-range operational weather forecasting for Ireland. The ECO-SG urban classification implicitly includes building heights. The work presented in this paper involved the production of the first open-access building height map for the island of Ireland which complements the Ulmas-Walsh land cover map, a map which has improved the horizontal extent of urban areas over Ireland. The resulting building height map will potentially enable upgrades to ECO-SG urban information for future implementation in HARMONIE-AROME. This study not only produced the first open-access building height map of Ireland at 10 m × 10 m resolution, but assessed various types of regression models trained using pre-existing building height information for Dublin City and selected 64 important spatio-temporal features, engineered from both the Sentinel-1A/B and Sentinel-2A/B satellites. The performance metrics revealed that a Convolutional Neural Network is superior in all aspects except the computational time required to create the map. Despite the superior accuracy of the Convolutional Neural Network, the final building height map created results from the ridge regression model which provided the best blend of realistic output and low computational complexity. The method relies solely on freely available satellite imagery, is cost-effective, can be updated regularly, and can be applied to other regions depending on the availability of representative regional building height sample data.

The Meteorology of the Tathra Bushfire

Abstract The meteorological conditions over the South Coast of New South Wales, Australia, are investigated on 18 March 2018, the day of the Tathra bushfire. We present an analysis of the event based on high-resolution (100- and 400-m grid-length) simulations with the Bureau of Meteorology’s ACCESS numerical weather prediction system and available observations. Through this analysis we find several mesoscale features that likely contributed to the extreme fire event. Key among these was the development of horizontal convective rolls, which emanated from inland and aided the fire’s spread toward Tathra. The rolls interacted with the terrain to produce complex regions of strongly ascending and descending air, likely accelerating the lofting of firebrands and potentially contributing to the significant lee-slope fire behavior observed. Mountain waves, specifically trapped lee waves, occurred on the day and are hypothesized to have contributed to the strong winds around the time the fire began. These waves may also have influenced conditions during the period of peak fire activity when the fire spotted across the Bega River and impacted Tathra. Finally, the passage of the cold front through the fireground was complex, with frontal regression observed at a nearby station and likely also through Tathra. We postulate that interactions between the strong prefrontal flow and the initially weak change resulted in highly variable and dangerous fire weather across the fireground for a significant period after the change initially occurred. Significance Statement The town of Tathra on the South Coast of New South Wales, Australia, was devastated on 18 March 2018, when a wildfire ignited in nearby bushland and quickly intensified to impact the town. Using high-resolution numerical weather simulations, we investigate the conditions that led to the extreme fire behavior. The simulations show that the fire ignited and intensified under highly variable conditions driven by complex interactions between the flow over nearby mountains and the passage of a strong cold front. This case study highlights the value of such models in understanding high-impact weather for the purpose of hazard preparedness and emergency response. Additionally, it contributes to a growing number of case studies that indicate the future direction of high-impact forecast services.

Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications

AbstractIn most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for the behavior of the random variables/errors that are involved. At the Cooperative Institute for Research in the Atmosphere theory has been developed for different forms of variational data assimilation schemes that enables the Gaussian assumption to be relaxed. For certain variable types, a lognormally distributed random variable can be combined in a mixed Gaussian‐lognormal distribution to better capture the interactions of the errors of different distributions. However, assuming that a distribution can change in time, then developing techniques to know when to switch between different versions of the data assimilation schemes becomes very important. By dynamically changing the formulation of the data assimilation system we are able to assimilate observations in a way that reflects the flow‐dependent variability of their distribution.In this paper, we present results with a machine learning technique (the support vector machine) to switch between data assimilation methods based on the detection of a change in the Lorenz 1963 model's z component's probability distribution. Given the machine learning technique's detection/prediction of a change in the distribution, then either a Gaussian or a mixed Gaussian‐lognormal 3DVar based cost function is used to minimize the errors in this period of time. It is shown that switching from a Gaussian 3DVar to a lognormal 3DVar at lognormally distributed parts of the attractor improves the data assimilation analysis error compared to using one distribution type for the entire attractor.

Impact of the Aeolus Level‐2B horizontal line‐of‐sight winds in the Environment and Climate Change Canada global forecast system

AbstractThe European Space Agency Aeolus mission was launched in August 2018. This satellite carries the first Doppler lidar able to provide global measurements of wind profiles. Aeolus Level‐2B products have been generated and monitored by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in near real‐time since a few weeks after the launch. These products include the horizontal line‐of‐sight (HLOS) winds that are suitable for data assimilation in numerical weather prediction systems. This article presents a series of observing system experiments conducted over summer 2019 to assess the value of the Level‐2B HLOS winds and their impact on the Environment and Climate Change Canada global forecasts. The impact of atmospheric motion vectors (AMVs) on forecasts is also examined and compared with the impact of HLOS winds. Two datasets are used: the HLOS winds produced in near real‐time at ECMWF and those reprocessed later in fall 2020. It is found that the near real‐time data are significantly biased and should be corrected. A look‐up table bias correction based on observation minus background departures is applied to this dataset as initially proposed by ECMWF. The reprocessed data are of better quality and bias corrected using the telescope's primary mirror temperature variations as predictor. The impacts of the near real‐time and reprocessed HLOS winds on forecasts are generally positive for both temperature and wind. The impacts are largest in the troposphere over the Tropics and polar regions. The positive impacts on forecasts are larger with the reprocessed data, particularly in the stratosphere, where a significant degradation over the Southern Hemisphere is found from assimilating the near real‐time data. The normalized forecast error reductions at days 1 and 2 for the wind are ∼1.25% over the Tropics and Southern Hemisphere. The positive impact of the HLOS winds on forecasts is enhanced by ∼40% when the AMVs are not assimilated in the control experiment. The forecast error reduction from assimilating AMVs is, however, two times larger than from assimilating HLOS winds in the extratropics. Conversely, the impact of HLOS winds on forecasts is generally larger in the Tropics.

A channel selection for the assimilation of CrIS and HIRAS instruments at full spectral resolution

AbstractA new channel selection is proposed for the processing of observations at full spectral resolution (FSR) from the Cross‐track Infrared Sounder (CrIS) and Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments in the Met Office global numerical weather prediction (NWP) system. The new selection has been derived in order to minimise the error in NWP analysis and has been compared to an existing selection developed at the National Oceanic and Atmospheric Administration (NOAA). Both selections have been tested in the Met Office global NWP system to investigate the use of FSR CrIS compared with the current normal spectral resolution (NSR) set‐up employed in operations. Improvements are obtained for most forecast variables up to 7‐day lead times, with change in root‐mean‐square error (RMSE) ranging from 0.13 to 0.54%, although degradation of tropical temperatures are noted for the NOAA selection. The background fit to observations from independent sounders improves by up to 0.8% with the new selection, outperforming the NOAA selection which results in degradations across most instruments. The assimilation of HIRAS observations does not however benefit the system.

Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next‐day maximum temperature in South Korea

AbstractRegression models for model output statistics (MOS) based on least absolute shrinkage and selection operator methods were developed to forecast next‐day maximum surface air temperature (TMAX) during the warm season in South Korea. The forecast fields from the operational numerical weather prediction (NWP) system of the Korean Meteorological Administration for global and local forecasts and the observed TMAX data in 225 observation stations were used as input variables for the MOS. The training period was July and August (JA) from 2015 to 2018, and the regression models were tested using data from JA 2019. As a result of hindcasting for the test period, the MOS models performed significantly better for next‐day TMAX forecasting over South Korea than the numerical models during JA 2019. The mean TMAX errors were reduced by over 1°C in MOSs compared to those in the numerical models. However, the TMAX forecast performance was generally lower in the higher‐resolution NWP Local Data Assimilation and Prediction System (LDAPS)‐based MOS (LMOS) than in the lower‐resolution NWP Global Data Assimilation and Prediction System (GDAPS)‐based MOS. This pattern was dominant when LDAPS simulated the TMAX more accurately than average. In particular, the random TMAX error of LDAPS was larger than that of GDAPS during the training period, and a positive random error of TMAX was magnified in LMOS. Because the other predictors forecasted from LDAPS can be associated with lower TMAX forecast performance of LMOS, in addition to TMAX effects as a predictor, a new MOS was developed using both LDAPS and GDAPS outputs. The forecast accuracy was improved by up to 0.3°C when the forecast fields from the GDAPS substituted several LMOS predictors, even though TMAX was the primary predictor for LMOS.

Reducing the spin‐up of a regional NWP system without data assimilation

AbstractIn both operational and research settings, kilometre‐scale regional numerical weather prediction (NWP) models are often initialised by interpolating analyses from a global modelling system on to the finer regional grid. When initialised in this way (known as a cold start), the first few hours of the forecast are characterised by a rapid growth of precipitation as the system develops convective‐scale structures. This is the well‐known spin‐up problem. Spin‐up effects limit the utility of cold‐start models for short‐range forecasting of severe weather events and model development activities. In this article, we present a method for reducing the spin‐up of regional NWP models, without data assimilation (DA). The basis of our method is periodically to insert large‐scale information from global model analyses into a continuously cycling regional model, thus updating the large scales whilst preserving fine‐scale structures. We refer to this as a warm start. Our method is tested in a regional model over Darwin, for a 10‐week period in the 2016/2017 wet season. It is shown that warm‐starting indeed reduces the spin‐up of precipitation significantly in the first 6–12 hr of the forecast compared with cold‐starting. Full objective verification against both radar and satellite observations reveals that precipitation forecast skill is also improved during this time. In addition, the large scales remain closer to global analyses (taken as a best guess of reality) than in a free‐running model in which the large scales are updated only via the lateral boundary conditions. Our warm‐start technique thus provides a considerable improvement on standard cold‐starting and could also be integrated into a regional modelling system with full convective‐scale DA as a means of reducing analysis errors on large scales.

Improvements on short-term precipitation forecast in Northwest China based on regionally optimized moisture adjustment scheme for convective-scale NWP

Initial moisture information is of significance in high-resolution numerical weather prediction (NWP). To enhance the forecasting ability of kilometric-scale NWP systems, the performance of two moisture adjustment schemes (MASs) in a cloud analysis system were first investigated in the complex terrain. Subsequently, a three-step approach to reconstruct a suitable MAS for the local NWP was proposed. The effectiveness and ability of the reconstructed scheme were examined with a series of forecast experiments involving eight heavy rain cases and a one-month period in summer 2018. All experiments, with or without cloud analysis, were conducted using the GRAPES-Meso model with a resolution of 3 km and the operational setting pertaining to Northwest China. The eight heavy rain cases and the one-month period experiments demonstrated that the reconstructed MAS outperforms the MAS currently used in operational NWP, even in the complex terrain. The reconstructed MAS can achieve superior precipitation forecasts, with a high forecast accuracy for 2 m temperature and 10 m wind, especially for eight heavy rain cases. Notably, the precipitation forecast performance is sensitive to the parameters in the moisture adjustment scheme. The performance of the reconstructed MAS for eight heavy rain cases is better than that for the one-month period experiments, likely because the data samples used to reconstruct MAS are extracted from the forecasts of one heavy rain case. The three-step reconstructed approach for the moisture adjustment scheme can be extended into other regional cloud analysis systems.

Control simulation experiment with Lorenz's butterfly attractor

Abstract. In numerical weather prediction (NWP), sensitivity to initial conditions brings chaotic behaviors and an intrinsic limit to predictability, but it also implies an effective control in which a small control signal grows rapidly to make a substantial difference. The Observing Systems Simulation Experiment (OSSE) is a well-known approach to study predictability, where “nature” is synthesized by an independent NWP model run. In this study, we extend the OSSE and design the control simulation experiment (CSE), where we apply a small signal to control “nature”. Idealized experiments with the Lorenz-63 three-variable system show that we can control “nature” to stay in a chosen regime without shifting to the other, i.e., in a chosen wing of Lorenz's butterfly attractor, by adding small perturbations to “nature”. Using longer-lead-time forecasts, we achieve more effective control with a perturbation size of less than only 3 % of the observation error. We anticipate our idealized CSE to be a starting point for a realistic CSE using the real-world NWP systems, toward possible future applications to reduce weather disaster risks. The CSE may be applied to other chaotic systems beyond NWP.

Verification by Multiple Methods of Precipitation Forecast from HDRFFGS and SisPI Tools during the Impact of the Tropical Storm Isaias over the Dominican Republic

During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, tropical storm Isaias stands out because of the significant precipitation (327.6 mm at Sabana del Mar during 29–31 July 2020) and flooding it caused. The study analyzes the behavior of the products of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the precipitation generated by Isaias on 30 July 2020 over the Dominican Republic. Traditional categorical verification and featured-based spatial verification methods are used in the study, taking as observation the quantitative precipitation estimation of GPM. The results show that both numerical weather prediction systems are powerful tools for QPF and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. For the forecast of rain occurrence, the HIRESW-NMMB product of FFGS presented the highest ability with a CSI greater than 0.4. The HIRESW-ARW and SisPI products not only presented high rates of false alarms but also performed better in forecasting heavy rain values. The results of the verification based on objects with the MODE are consistent with those obtained in the verification by categories. The HIRESW-NMMB product underestimated the intense rainfall values by approximately 60 mm, while HIRESW-ARW and SisPI tools presented minor differences, the latter being the one with the greatest skill.