Flash flood forecasting within the PREVIEW project: value of high-resolution hydrometeorological coupled forecast

A hydrometeorologic forecast system (HFS) has been developed that takes advantage of new high-resolution rainfall datasets from the WSR-88D radar system, the Oklahoma Mesonet, and Oklahoma Local Analysis and Prediction System (OLAPS). New schemes to analyze precipitation and to adjust radar rainfall rates have been proposed to improve the quantitative precipitation forecast (QPF) for hydrologic purposes. Adjusted WSR-88D rainfall rates were advected by the 700-mb wind field from OLAPS to produce an extrapolation QPF. Several experiments were conducted to evaluate the effect of the rainfall adjustment and wind field upon the extrapolation QPF. In addition, mesoscale model‐produced QPFs were generated using The Pennsylvania State University‐ National Center for Atmospheric Research Mesoscale Model. Control and rainfall assimilation experiments were performed using both Kuo and Kain‐Fritsch cumulus parameterization schemes for three rainfall events from April 1994. All model runs were integrated forward 12 h and then verified against the analyzed precipitation field. Both the extrapolation and model-produced QPFs were used to produce hydrologic forecasts for the Dry Creek watershed in north-central Oklahoma. Results indicate that extrapolation QPFs degrade exponentially with time and become inferior to the QPF from a mesoscale model after 2 h. When the extrapolated rainfall estimates were input into a hydrologic model, an underestimate of the peak flow occurred since the time evolution of precipitating systems is not handled by extrapolation. Due to the lag time between the peak in precipitation and the peak in streamflow, the greatest impact upon the accuracy of hydrologic forecasts resulted from improvements in the analyzed precipitation field. On the other hand, mesoscale forecast simulations revealed that the assimilation of analyzed rainfall had a limited impact upon the evolution of model-produced precipitation forecasts out to 4 h. However, model-produced QPFs improved after 8 h into the integration. While the Kuo scheme produced less dispersion error, the Kain‐ Fritsch scheme created less amplitude error. The assimilation of analyzed rainfall through the convergence factor of the Kuo scheme had a greater impact upon the performance of the mesoscale model than did the Kain‐Fritsch rainfall assimilation through the adjustment of its precipitation efficiency factor. Therefore, a new generation HFS has been developed to take advantage of new technology and new scientific methods in an attempt to mitigate the age-old issue of devastating floods that occur without warning. Each component has been tested and evaluated. The results of testing and evaluating each component of the proposed HFS are presented in this paper.

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Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.

Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding

The purpose of this study is to present the possibilities offered by coupled atmospheric and hydrologic models as a new tool to validate and interpret results produced by atmospheric models. The advantages offered by streamflow observations are different from those offered by conventional precipitation observations. The dependence between basins and subbasins can be very useful, and the integrating effect of the large basins facilitates the evaluation of state-of-the-art atmospheric models by filtering out some of the spatial and temporal variability that complicate the point-by-point verifications that are more commonly used. Streamflow permits a better estimate of the amount of water that has fallen over a region. A comparison of the streamflow predicted by the coupled atmospheric–hydrologic model versus the measured streamflow is sufficiently sensitive to clearly assess atmospheric model improvements resulting from increasing horizontal resolution and altering the treatment of precipitation processes in the model. A case study using the WATFLOOD hydrologic model developed at the University of Waterloo is presented for several southern Ontario river basins. WATFLOOD is one-way coupled to a nonhydrostatic mesoscale atmospheric model that is integrated at horizontal resolutions of 35, 10, and 3 km. This hydrologic model is also driven by radar-derived precipitation amounts from King City radar observations. Rain gauge observations and measured streamflows are also available for this case, permitting multiple validation comparisons. These experiments show some uncertainties associated with each tool independently, and also the interesting complementary nature of these tools when they are used together. The predicted precipitation patterns are also compared directly with rain gauge observations and with radar data. It is demonstrated that the hydrologic model is sufficiently sensitive and accurate to diagnose model and radar errors. This tool brings an additional degree of verification that will be very important in the improvement of technologies associated with atmospheric models, radar observations, and water resource management.

The predictability of precipitation is hindered by finer-scale processes not captured explicitly in global numerical models, such as convective interactions, cloud microphysics, and boundary layer dynamics. However, there is growing demand across various sectors for medium- (3–10-day) and extended-range (10–30-day) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs). This study uses a novel statistical postprocessing technique, APPM, that combines analog postprocessing (AP) with probability matching (PM) to produce week-1 and week-2 accumulated precipitation forecasts over Taiwan. AP searches for historical predictions that closely resemble the current forecast and create an AP ensemble using the observed high-resolution precipitation patterns corresponding to these forecast analogs. Frequency counting and PM are then separately applied to the AP ensemble to produce calibrated and downscaled PQPFs and bias-reduced QPFs, respectively. Evaluation over a 22-yr (1999–2020) period shows that raw ensemble forecasts from the GEFS of NOAA/NWS/Environmental Modeling Center, collected for the subseasonal experiment, are underdispersive with a wet bias. In contrast, the AP ensemble spread well represents forecast uncertainty, leading to substantially more reliable and skillful probabilistic forecasts. Furthermore, the AP-based PQPF demonstrates superior discrimination ability and yields notably greater economic benefits for a wider range of users, with the maximum economic value increasing by 30%–50% for the week-2 forecast. Compared to the raw ensemble mean forecast, the calibrated QPF exhibits lower mean absolute error and explains 3–8 times more variance in observations. Overall, the APPM technique significantly improves week-1 and week-2 QPFs and PQPFs over Taiwan. Significance Statement There are two significant challenges in improving precipitation forecasts beyond a few days in Taiwan. First, large-scale numerical models often struggle with accurately predicting precipitation locations, magnitudes, and providing sufficient detail. Second, probabilistic precipitation forecasts have been unreliable, failing to convey accurate uncertainty information to users. In response to these challenges, this study has developed a relatively simple yet effective technique that corrects the spatiotemporal distribution of predicted precipitation and downscales the forecasts from a 1° to 1-km spatial resolution. Our results demonstrate that this technique significantly alleviates these two issues, resulting in more accurate precipitation forecasts and more reliable probabilistic precipitation forecasts within a 2-week timeframe.

Quantitative hydrologic forecasting usually requires knowledge of the spatial and temporal distribution of precipitation. First, it is important to accurately measure the precipitation falling over a particular watershed of interest. Second, especially for small watersheds and/or for longer forecast lead times, forecasts of precipitation are critical to the achievement of the greatest possible hydrologic forecast accuracy and longest possible lead time. This paper describes the current hydrologic forecasting program of the U.S. National Weather Service (NWS) and highlights the relevance of Quantitative Precipitation Forecasting (QPF) products to real-time hydrologic forecasting. Specific requirements for QPF products in support of hydrologic forecasting applications are defined and current operational QPF procedures are reviewed to determine to what extent they meet these requirements. It is concluded that no known QPF procedures capable of fulfilling all desired requirements are currently available operationally, although much valuable QPF information is available to meet parts of these requirements. Some recent advances in mesoscale QPF research are examined and these techniques are treated in two categories: those uncoupled dynamically from and those dynamically coupled to hydrologic forecasting procedures. Finally, a summary of possible future directions toward achieving improved use of QPF information in hydrologic forecasting applications is presented.

<p>For every atmospheric radionuclide sample taken by the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO), the CTBTO makes use of operational Atmospheric Transport Modelling (ATM) to assist States Signatories in localization of possible source regions of any measured substance. Currently, ATM is accomplished by using the Lagrangian particle dispersion model (LPDM) FLEXPART driven by global meteorological fields with a spatial resolution of 0.5 degrees and 1 hourly temporal resolution. Meteorological fields are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF ) and the National Centers for Environmental Prediction (NCEP).  </p><p>Recent studies to increase the accuracy in the CTBTO’s localization process to be applied for specific detection events, utilizes High-Resolution Atmospheric Transport Modelling (HRATM) by using the Weather Research and Forecasting model (WRF) to generate high-resolution meteorological input data for the LPDM version Flexpart-WRF.   </p><p>This presentation uses measurements from the International Monitoring System (IMS) station DEX33 in Germany of seven episodes of elevated Xe-133 concentrations in 2014 in combination with with the stack emission data of the medical isotope production facility IRE in Fleurus, Belgium. Each episode consists of 6 to 11 subsequent 24-hour samples. Backward simulations for each sample are conducted and the sensitivity to the stack emission data are analysed. All samples determined to represent a detection of IRE releases are selected to be used for an evaluation study. </p><p>Evaluating the CTBTO’s utilization of HRATM requires to investigate the ability to localize the source region as well as the accuracy of the match and the computational performance to accomplish these results. The evaluation of HRATM results is done by using statistical metrics established during former ATM challenges. Concerning the computational performance and to account for uncertainties, sensitivity studies with varying spatial resolutions, physical parameterization variations and different regional domain setups for WRF were accomplished. This comprises a reference comparison to the operational ATM FLEXPART model with an increased spatial resolution to 0.1 degrees.   </p>

Abstract. Understanding the exchange processes between the atmospheric boundary layer and the free troposphere is crucial for estimating hemispheric transport of air pollution. Most studies of hemispheric air pollution transport have taken a large-scale perspective using global chemical transport models with fairly coarse spatial and temporal resolutions. In support of United Nations Task Force on Hemispheric Transport of Air Pollution (TF HTAP; www.htap.org), this study employs two high-resolution atmospheric chemistry models (WRF-Chem and CMAQ; 36×36 km) driven with chemical boundary conditions from a global model (MOZART; 1.9×1.9°) to examine the role of fine-scale transport and chemistry processes in controlling pollution export and import over the Asian continent in spring (March 2001). Our analysis indicates the importance of rapid venting through deep convection that develops along the leading edge of frontal system convergence bands, which are not adequately resolved in either of two global models compared with TRACE-P aircraft observations during a frontal event. Both regional model simulations and observations show that frontal outflows of CO, O3 and PAN can extend to the upper troposphere (6–9 km). Pollution plumes in the global MOZART model are typically diluted and insufficiently lofted to higher altitudes where they can undergo more efficient transport in stronger winds. We use sensitivity simulations that perturb chemical boundary conditions in the CMAQ regional model to estimate that the O3 production over East Asia (EA) driven by PAN decomposition contributes 20% of the spatial averaged total O3 response to European (EU) emission perturbations in March, and occasionally contributes approximately 50% of the total O3 response in subsiding plumes at mountain observatories (at approximately 2 km altitude). The response to decomposing PAN of EU origin is strongly affected by the O3 formation chemical regimes, which vary with the model chemical mechanism and NOx/VOC emissions. Our high-resolution models demonstrate a large spatial variability (by up to a factor of 6) in the response of local O3 to 20% reductions in EU anthropogenic O3 precursor emissions. The response in the highly populated Asian megacities is 40–50% lower in our high-resolution models than the global model, suggesting that the source-receptor relationships inferred from the global coarse-resolution models likely overestimate health impacts associated with intercontinental O3 transport. Our results highlight the important roles of rapid convective transport, orographic forcing, urban photochemistry and heterogeneous boundary layer processes in controlling intercontinental transport; these processes may not be well resolved in the large-scale models.

Floods are one of the most devastating natural hazards around the world, and flash floods are extremely dangerous to property and human lives. This study reviews the current flash flood forecasting in China and proposes a new methodology to develop a flash flood potential index (FFPI) based on GIS to further improve China's flash flood guidance system and national weather forecasting services. We collected datasets required to calculate the FFPI and tested a series of schemes to determine the method to calculate the FFPI over China. The result indicates that the calculated China's FFPI distribution can represent the risk of flash flood well, especially in Central and Southeast China. It is also implied that the southwest and western complex terrains are more susceptible to rain storms. Then we chose Yunnan Province as a test-bed and also yielded good results which can offer reference to the current China's flash flood forecasting system. Further research can be placed on incorporation of soil moisture and rainfall frequency components into the FFPI calculation method for dynamic warning and forecasting of flash floods.

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.

Abstract. Accurate continuous measurements of relative humidity (RH) vertical profiles in the lower troposphere have become a significant scientific challenge. In recent years a synergy of various ground-based remote sensing instruments have been successfully used for RH vertical profiling, which has resulted in the improvement of spatial resolution and, in some cases, of the accuracy of the measurement. Some studies have also suggested the use of high-resolution model simulations as input datasets into RH vertical profiling techniques. In this paper we apply two synergetic methods for RH profiling, including the synergy of lidar with a microwave radiometer and high-resolution atmospheric modeling. The two methods are employed for RH retrieval between 100 and 6000 m with increased spatial resolution, based on datasets from the HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) campaign conducted in Athens, Greece from May to June 2014. RH profiles from synergetic methods are then compared with those retrieved using single instruments or as simulated by high-resolution models. Our proposed technique for RH profiling provides improved statistical agreement with reference to radiosoundings by 27 % when the lidar–radiometer (in comparison with radiometer measurements) approach is used and by 15 % when a lidar model is used (in comparison with WRF-model simulations). Mean uncertainty of RH due to temperature bias in RH profiling was ∼ 4.34 % for the lidar–radiometer and ∼ 1.22 % for the lidar–model methods. However, maximum uncertainty in RH retrievals due to temperature bias showed that lidar-model method is more reliable at heights greater than 2000 m. Overall, our results have demonstrated the capability of both combined methods for daytime measurements in heights between 100 and 6000 m when lidar–radiometer or lidar–WRF combined datasets are available.

Typical state of the art atmospheric general circulation models used in climate change studies have horizontal resolution of approximately 300 km. As computing power increases, many climate modeling groups are working toward enhancing the resolution of global models. An important issue that arises when resolution of a model is changed is whether cloud and convective parameterizations, which were developed for use at coarser resolutions, will need to be reformulated or re-tuned. We propose to investigate this issue and specifically cloud statistics using ARM data. The data streams produced by highly instrumented sections of Cloud and Radiation Testbeds (CART) of ARM program will provide a significant aid in the evaluation of cloud and convection parameterization in high-resolution models. Recently, we have performed multiyear global-climate simulations at T170 and T239 resolutions, corresponding to grid cell sizes of 0.7{sup 0} and 0.5{sup 0} respectively, using the NCAR Community Climate Model. We have also a performed climate change simulation at T170. On the scales of a T42 grid cell (300 km) and larger, nearly all quantities we examined in T170 simulation agree better with observations in terms of spatial patterns than do results in a comparable simulation at T42. Increasing the resolution to T239 brings significant further improvement. At T239, the high-resolution model grid cells approach the dimensions of the highly instrumented sections of ARM Cloud and Radiation Testbed (CART) sites. We propose to form a cloud climatology using ARM data for its CART sites and evaluate cloud statistics of the NCAR Community Atmosphere Model (CAM) at higher resolutions over those sites using this ARM cloud climatology. We will then modify the physical parameterizations of CAM for better agreement with ARM data. We will work closely with NCAR in modifying the parameters in cloud and convection parameterizations for the high-resolution model. Our proposal to evaluate the cloud parameterization at high resolution has high relevance for the mission of ARM (to improve the treatment of clouds and radiation in the models) because the high-resolution model grid cells approach the dimensions of the highly instrumented sections of ARM CART sites.

Flash floods account for a large proportion of flood-based fatalities, and they are becoming more frequent due to climate change. A global flash flood warning system therefore has the potential to be life saving.Standard approaches to flash flood forecasting – such as the Flash Flood Guidance (FFG) system in the US National Weather Service (NWS), or the European Runoff Index based on Climatology (ERIC) in the European Flood Awareness System (EFAS) – are utilizing recent weather and soil conditions, physiographic characteristics of basins, and weather forecasts, in order to produce a forecast for possible flash floods. These forecasts are not disseminated directly to the public. Instead, they are firstly refined by hydrologists that have intimate knowledge of the relevant basins and of previous flood events. This introduces a difficulty to scaling these methods worldwide, as the training of professional hydrologists in every region is costly and time consuming.Recent applications of Machine Learning (ML) to hydrology show that a learning system has the potential to train on data-rich basins and generalize to data-poor basins, with a skill that is comparable to state of the art hydrological models. In this work we attempt to build a ML model to produce daily flash flood forecasts, based on globally available weather reanalysis and physiographic characteristics from HydroATLAS. We discuss the model architecture, and evaluate it against NWS Flash Flood Warnings (FFW). While such models may not surpass the skill of a professional hydrologist, they have the potential to provide reasonable warnings in regions that do not currently have any such system in place.