Numerical Forecast Models Research Articles

Synergistic Integration of Detailed Meteorological and Community Information for Evacuation from Weather-Related Disasters: Proposal of a \u201cDisaster Response Switch\u201d

Meteorological information used for disaster prevention has developed rapidly in terms of both type and specificity. The latest forecasting models can predict weather with very high resolutions that can characterize disaster risk at the local level. However, this development can lead to an overdependency on the information and a wait-and-see attitude by the public. At the same time, residents share and use various types of information for disaster response, such as local conditions, in addition to official disaster information. Our research in Japan verified the practicality and efficiency of synergistically integrating these types of information by examining actual evacuation cases. The current numerical forecasting models sufficiently identify locality from the viewpoint of various administrative scales such as prefectures, municipalities, and school districts, but the improvements to these models have failed to improve residents’ judgment in successful evacuation cases. We therefore analyzed the relationship between meteorological information and residents’ disaster response and confirmed that they were strongly correlated and were contributing factors in preventing disasters. We revealed differences between a community’s disaster prevention culture and the disaster information provided. This led us to propose a new concept in community disaster prevention that we call the “disaster response switch,” which can serve as a data-driven risk management tool for communities when used in combination with advanced meteorological disaster information.

Information gain-based modular fuzzy neural network to forecast rainstorms

Abstract This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is introduced to the predictor processing of the forecast model. Then the characteristics of many rainstorm predictors are calculated and screened on the basis of feature weight, information is condensed, some non-correlated forecast information variables are extracted, and the network structure of the forecast model is optimized. The modeled samples are determined and reconstructed by setting thresholds, and the modular forecast models of heavy rainfall and weak rainfall are established. The actual forecast results of the 24 h experimental prediction of the independent samples of large-scale rainstorms in Guangxi in 2012–2016 showed that the information gain-based modular FNN rainstorm forecasting model has higher prediction accuracy and a more stable forecasting effect. The various types of scores of 24 h of rainstorm (≧50 mm) at 89 weather stations in Guangxi from 2012 to 2016 are: threat score (TS) is 0.368, ETS: equal threat score (E) is 0.141, hit rate (POD) is 0.296, empty report rate (FAR) is 0.559, forecast bias (B) is 0.671, and HSS skill score (H) is 0.247. Further comparison and analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecasting model forecast results indicated that the new model performed nonlinear intelligence calculated interpretation modeling on ECMWF numerical forecasting model products, and forecasting accuracy is improved to a certain extent compared with that of the original model. Forecasting techniques are positive and have good release effects, thereby improving the rain forecasting ability of ECMWF to a certain extent and providing a better reference value for business forecasters.

An Operational Multi-Radar Multi-Sensor QPE System in Taiwan

AbstractOver the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

Statistical downscaling with spatial misalignment: Application to wildland fire PM2.5 concentration forecasting.

Fine particulate matter, PM2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM2.5 air pollution in the US. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.

Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model

Nowcasting is mainly based on radar echo or satellite image extrapolation method. However, the prediction ability of extrapolation method decreases with time, because this method cannot describe the physical mechanism during the occurrence, development and extinction of severe convective weather systems. Considering that the prediction ability of numerical model improves with time, the nowcasting system should be based on numerical forecast model. And appropriate data assimilation technology can be used to produce a more accurate initial field, making the integral forecast results closer to the reality. The PFI-4DVar assimilation method (four-dimensional variational technology under physical filter initialization) can filter in the process of assimilation rather than model integration, thus shortening the model spin-up time and getting a more dynamic and physically coordinated analysis field. Therefore, PFI-4DVar assimilation method not only improves model prediction results, but also makes initial field closer to observations, which is very suitable for nowcasting.Using WRF model and WRFDA assimilation system, effects of PFI-4DVar on prediction ability of numerical nowcasting are explored. Through the precipitation case in North China on 11 August 2018, prediction results in control and assimilation tests are discussed. According to ETS scores, the precipitation prediction of assimilation test is closer to the observation compared with control test. The water vapor in assimilated ground and sounding data, the dynamic field in assimilated radar radial wind data and the appropriate cumulus parameterization scheme make the amplitude of divergence in high-level and convergence in low-level in analysis field of assimilation test much stronger than those in background field, thus creating vertical motion. Moreover, the precipitation of assimilation test is mainly caused by process of cumulus.A batch test is carried out on 17 precipitation cases of North China in August 2018. It shows that PFI-4DVar can significantly improve the prediction ability for short precipitation (especially large order precipitation) and timely predict the fall area of heavy rain or rainstorm. After assimilation, ETS scores of 6-hour accumulated precipitation (greater than 25.0 mm) in batch test increase from 0.125 to 0.190, and ETS scores of 6-hour accumulated precipitation (greater than 60.0 mm) increase from 0.016 to 0.081. PFI-4DVar significantly improves the precipitation nowcasting. Calculations are reduced by selecting 12-minute assimilation time window, which greatly saves computational resources. And the time of assimilation test is shortened, ensuring the time efficiency of 6-hour forecast. Therefore, PFI-4DVar can improve and enhance the prediction ability of precipitation nowcasting.

Assessing forecasting performance of daily reference evapotranspiration using public weather forecast and numerical weather prediction

Accurate estimation of reference evapotranspiration (ET0) is important for water resource management and irrigation scheduling design. Limited by data availability of the numerical weather prediction (NWP), the public weather forecasting (PWF) has been widely used to forecast daily ET0 in China. Recently, two Chinese independently developed NWP products have been open to the public, namely, the Global/Regional Assimilation and Prediction System (GRAPES) and the T639 Global Medium-term Numerical Forecast Model (T639L60). In this paper, the forecast of ET0 is evaluated based on the recently released NWP outputs and the PWF using the FAO-56 PM equation. The daily ET0 forecast of the two datasets was compared against the ET0 that was calculated using weather variables observed from 31 automatic weather stations across a wide range of climate zones in China, including the temperate continental zone (TC), temperate monsoon zone (TM), mountain plateau zone (MP) and subtropical monsoon zone (SM). The results showed that the forecast performance of NWP for maximum and minimum air temperature (Tmax, Tmin) was worse due to the large errors in the altitude estimation process. The forecast performance of NWP for net radiation (Rnet) forecasted was better than that of PWF in the MP, TC and TM, while slightly worse in the SM. The VPD from the NWP was more accurate in TC and TM, while showed little difference with that from PWF in MP and SM. For the wind speed (u2), the NWP showed better forecast accuracy than PWF in all climate zones. Furthermore, the better forecast performance of ET0 was obtained by the NWP in the MP and TC. In the TM and SM, the PWF can provide a more accurate ET0 forecast compared to the NWP. Overall, the NWP can serve as a promising data source to generate acceptable ET0 forecasts across China. The high spatial resolution of the NWP provides the ability to capture the high resolution of the spatial variability in ET0, especially in the MP and TC where weather stations are sparse.

Objective approach for rainstorm based on dual-factor feature extraction and generalized regression neural network

Rainstorm often causes inland flooding and mudslides that threaten lives and properties. In this study, rainstorm is used as a forecasting object, and an interpretation prediction model for rainstorm based on the European Center for medium-range weather forecasting (ECMWF) numerical prediction model is constructed through the generalized regression neural network method. Model inputs are forecasted through principal component analysis, and dual-factor feature extraction is performed on the primary predictors to obtain new irrelevant variables and optimize network structures. The experimental forecast results of the 24 h aging test using an independent sample of large-scale rainstorm in Guangxi, China from 2012 to 2016, the actual forecast results of selected rainstorm cases with great influence on Guangxi, and different influencing systems show that the new prediction scheme is sophisticated. Thus, the scheme has a certain universal applicability. The results of the comparative analysis between the new program and ECMWF show that the forecasting ability of the new method is more accurate than that of the direct numerical forecasting model. The threat score of the new forecast model for 5 years has a 58.4% increase relative to that of the ECMWF. The forecasting skills are positive and good and can thus improve the rainstorm forecasting ability of ECMWF and provide a better guidance for forecasters.

Improved Severe Weather Forecasts Using LEO and GEO Satellite Soundings

AbstractIt is shown here that improvements in numerical weather prediction (NWP) model forecasts of hazardous weather can be obtained by assimilating profile retrievals obtained in real time from combined direct broadcast system (DBS) polar satellite hyperspectral and geostationary satellite multispectral radiance data. Results of NWP model forecasts are shown for two recent tornado outbreak cases: 1) the 3 March 2019 tornado outbreak over the southeast United States and 2) the tornado outbreak that occurred across Illinois, Indiana, and Ohio during the night of 27 May and the morning of 28 May 2019, and 3) the 4 March 2019 severe precipitation event that occurred in southeast China. Improvements in both quantitative precipitation forecasts (QPFs) and predictions of the location of tornado occurrence are obtained. It is also shown that geostationary satellite hyperspectral soundings [i.e.,Fengyun-4A(FY-4A) Geosynchronous Interferometric Infrared Sounder (GIIRS)] further improve hazardous precipitation forecasts when used, in addition to the combined polar hyperspectral and geostationary multispectral satellite profile data, to initialize the numerical forecast model. The lowest false alarm rate (FAR) and the highest probability of detection (POD) and critical success index (CSI) scores are achieved when assimilating atmospheric profile retrievals obtained by combining all the available satellite high-vertical-resolution hyperspectral radiance measurements with geostationary satellite high-spatial-resolution and high-temporal-resolution multispectral radiance measurements.

Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

Statistical models for improving significant wave height predictions in offshore operations

Installation and maintenance strategies regarding offshore wind farm operations involve extensive logistics. The main focus is the right temporal and spatial placement of personnel and equipment, while taking into account forecasted meteorological and ocean conditions. For these operations to be successful, weather windows characterized by certain permissive wave conditions are of enormous importance, whereas unforeseen events result in high cost and risk of safety. Numerical modelling of waves, water levels and current related variables has been used extensively to forecast ocean conditions. To account for the inherited model uncertainty, several error modelling techniques can be implemented for the numerical model forecasts to be corrected. In this study, various Bayesian Network (BN) models are incorporated, in order to enhance the accuracy of the significant wave height predictions and to be compared with other techniques, in conditions resembling the real-time nature of the application. The implemented BN models differ in terms of training and structure and provide overall the most satisfying performance. Supplementary, it is shown that the BN models illustrate significant advantages as both quantitative and conceptual tools, since they produce estimates for the underlying uncertainty of the phenomena, while providing information about the incorporated variables’ dependence relationships through their structure.

Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

The bow echo, a mesoscale convective system (MCS) responsible for much hail and wind damage across the United States, is associated with poor skill in convection-allowing numerical model forecasts. Given the decrease in convection-allowing grid spacings within many operational forecasting systems, we investigate the effect of finer resolution on the character of bowing-MCS development in a real-data numerical simulation. Two ensembles were generated: one with a single domain of 3-km horizontal grid spacing, and another nesting a 1-km domain with two-way feedback. Ensemble members were generated from their control member with a stochastic kinetic-energy backscatter scheme, with identical initial and lateral-boundary conditions. Results suggest that resolution reduces hindcast skill of this MCS, as measured with an adaptation of the object-based Structure–Amplitude–Location method. The nested 1-km ensemble produces a faster system than in both the 3-km ensemble and observations. The nested 1-km simulation also produced stronger cold pools, which could be enhanced by the increased (fractal) cloud surface area with higher resolution, allowing more entrainment of dry air and hence increased evaporative cooling.

Synoptic Analysis of Extreme Rainfall Event in West Africa: the Case of Linguère

After the drought period of the 70s and 80s, the Sahelian countries have experienced a resurgence of heavy rains phenomena and devastating floods causing a lot of socio-economic damages since the beginning of the 21st century. In this work, the environmental conditions associated with an extreme rainfall event that has led to high socio-economic impact in Senegal is studied by using the database of the extreme event from the DPC (Direction de la Protection Civil) of Senegal, Satellite products, ERA-Interim reanalysis and five Weather Model Prediction datasets. The rain event occurred on 26 August 2017 at Linguere (15.07°W and 15.23°N). This study aims to analyse the synoptic conditions associated to the event and also the ability of the numerical forecast models to predict it. The satellite dataset shows that the precipitating convective system was initiated at the level of a trough, on August 25 in the afternoon, and the extreme rain event took place on August 26 between 0600UTC and 1200UTC over Linguere. Various atmospheric parameters such as the configuration of the low-level moisture transport, precipitable water, relative humidity at 200 and 700-hPa as well as relative vorticity at 700-hPa appear as good indicators to characterize extreme rainfalls. The numerical forecast models used were able to predict short-term rainfall around Linguere. However, none of the models could predict the extreme aspect of precipitation because they tend to underestimate the intensity compared to rain gauge records.

An evaluation of operational and research weather forecasts for southern West Africa using observations from the DACCIWA field campaign in June–July 2016

AbstractReliable and accurate weather forecasts, particularly those of rainfall and its extremes, have the potential to improve living conditions in densely populated southern West Africa (SWA). The limited availability of observations has long impeded a rigorous evaluation of current state‐of‐the‐art forecast models. The field campaign of the Dynamics‐Aerosol‐Chemistry‐Cloud Interactions in West Africa (DACCIWA) project in June–July 2016 has created an unprecedentedly dense set of measurements from surface stations and radiosondes. Here we present results from a comprehensive evaluation of both numerical model forecasts and satellite products using these data on a regional and local level. Results reveal a substantial observational uncertainty showing considerable underestimations in satellite estimates of rainfall and low‐cloud cover with little correlation at the local scale. Models have a dry bias of 0.1–1.9 in rainfall and too low column relative humidity. They tend to underestimate low clouds, leading to excess surface solar radiation of 43 . Remarkably, most models show some skill in representing regional modulations of rainfall related to synoptic‐scale disturbances, while local variations in rainfall and cloudiness are hardly captured. Slightly better results are found with respect to temperature and for the post‐onset rather than for the pre‐onset period. Delicate local features such as the Maritime Inflow phenomenon are also rather poorly represented, leading to too cool, dry and cloudy conditions at the coast. Differences between forecast days 1 and 2 are relatively small and hardly systematic, suggesting a relatively quick error saturation. Using explicit convection leads to more realistic spatial variability in rainfall, but otherwise no marked improvement. Future work should aim at improving the subtle balance between the diurnal cycles of low clouds, surface radiation, the boundary layer and convection. Further efforts are also needed to improve the observational system beyond field campaign periods.

Comparison and Analysis of Two Different Calculation Schemes of SLEVE- Hybrid Coordinate in GRAPES_MESO Model

The calculation scheme of the smoothed-level and hybrid (SLEVE-hybrid for short) coordinates in numerical forecasting model is not limited in number. It is divided into the semi-analytical scheme and the finite differential scheme in terms of the various differential methods of the coordinate deformation variables. Having compared the dynamic equation and the long-time batch simulation results of the two schemes, the present study draws the following conclusions. The first-order finite difference accuracy of the coordinate deformation variables in the finite differential scheme is theoretically lower than that in the semi-analytical scheme. The larger the vertical gradient of the layer thickness is, the larger the relative errors of the finite differential scheme are. The long-time batch simulation test in the GRAPES model dynamic core demonstrates that the bias of the temperature and the geopotential height in the semi-analytical scheme is smaller under the default layering, while the simulation difference of the two schemes is greatly reduced when the layering is more uniform.

Estimating ground-level particulate matter concentrations using satellite-based data: a review

ABSTRACTParticulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol products such as aerosol optical depth (AOD) have been a useful source of data for ground-level PM monitoring. However, satellite-based approaches for PM monitoring have limitations such as impacts of cloud cover. Recently, many studies have documented advances in modeling for monitoring PM over the globe. This review examines recent papers on ground-level PM monitoring for the past 10 years focusing on modeling techniques, sensor types, and areas. Satellite-based retrievals of AOD and commonly used approaches for estimating PM concentrations are also briefly reviewed. Research trends and challenges are discussed based on the review of 130 papers. The limitations and challenges include spatiotemporal scale issues, missing values in satellite-based variables, sparse distribution of ground stations for calibration and validation, unbalanced distribution of PM concentrations, and difficulty in the operational use of satellite-based PM estimation models. The literature review suggests there is room for further investigating: 1) the spatial extension of PM monitoring to global scale; 2) the synergistic use of satellite-derived products and numerical model output to improve PM estimation accuracy, gap-filling, and operational monitoring; 3) the use of more advanced modeling techniques including data assimilations; 4) the improvement of emission data quality; and 5) short-term (hours to days) PM forecasts through combining satellite data and numerical forecast model results.

Characteristics of Road Surface Temperature in Beijing Winter and its statistic forecasting models Based on the RMAPS Product

An analysis of the inter-diurnal variation of road surface temperature in the different weather conditions in Beijing winter is done, based on the data of the road stations from 2012 to 2017 and the outcomes from the numerical forecasting model, Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS), which are constructed in this paper. First, the correlation coefficients between road surface temperature and the meteorological factors output by the RMPAS model are investigated. We used the stepwise regression model methods to build three kinds of types of statistical models for hourly road surface temperature in 24 h in winter. Then the best forecasting models are chosen to build for forecasting road minimum temperature in winter from A1027 road station selected. The results show that there exists a significant diurnal variation for the road surface temperature, suggesting that the road surface temperature is obviously different under the different kind of weather conditions. The road surface temperature is correlated with air temperature, atmospheric radiation, and sunshine duration. Compared to the type of statistical model with the only one factor for the road temperature of previous day, the type of regression model with meteorological elements of remarkable correlation inserted performs better in terms of the road surface temperature forecast accuracy by more than 25%, and the prediction error decreases by 1 °C.

Implementación de Mejoras Físicas y Químicas a un Sistema de Modelado de Calidad del Aire para la Argentina y su Validación, Utilizando Información Satelital.

La investigación y el monitoreo de calidad del aire fueron ganando iportancia en Argentina yLationamérica, principalmente en megaciudades donde la contaminación alcanza niveles críticos.Otro instrumento muy usado en el estudio de la composición atmosférica y sus procesos físicos yquímicos, y también para obtener el pronósstico de calidad de aire en una región, es un modelo de transporte químico. Este tipo de modelos estima las condiciones del aire que respiramos, la concentración de contaminantes en el nivel superficial y niveles superiores, y cómo sonconsumidos/producidos y transportados por las condiciones meteorológicas. El objetivo principal de la tesis es realizar mejoras en el sistema de modelado WRF-CHIMERE-EDGAR, ya implementado anteriormente en Argentina, que consideren una evaluación del sistema utilizando información satelital, y una implementación operativa para brindar pronóstico de calidad del aire a la población. El sistema se compone de un inventario de emisiones antropogénicas global EDGAR, el modelo de predicción numérica del tiempo WRF y el modelo de transporte químico CHIMERE. La evaluación del sistema fue realizada en términos de densidad diaria en columna total atmosférica de NO2, CO y espesor óptico de aerosoles, utilizando datos de los sensores OMI, MOPITT y MODIS, respectivamente. El dominio espacial definido incluye a toda la Argentina continental, y el temporal es de marzo a mayo del 2009 para evaluar NO2 en condiciones de emisiones regulares antropogénicas y al período diciembre 2016 a febrero 2017 para evaluar el destino de la emisión de contaminantes por incendios de vegetación en un gran evento en la Patagonia Nororiental. Para esto último, fue implementado el modelo de emisiones de incendios APIFLAME que estima la tasa de emisión para cada foco de incendios, en función de datos satelitales dinámicos y estáticos que brindan información sobre la ubicación, potencia radiativa, tipo de cobertura vegetal, cantidad de biomasa disponible y factores de emisión. La evaluación del sistema de modelado con información satelital da resultados optimistas en cuanto a la distribución espacial y temporal de los contaminantes presentes en la atmósfera, aunque aún hay una subestimación en las emisiones producto de los inventarios de emisiones antropogénicas y de incendios. Por otro lado, aún es necesaria una evaluación del perfil vertical de contaminantes. Finalmente, en el sitio de la CONAE http://meteo.caearte.conae.gov.ar/wrf/chimere.php ya se encuentra la implementación operativa del Pronóstico Experimental de Calidad del Aire WRF-CHIMERE-EDGAR, junto con la documentación descriptiva y publicaciones relacionadas; esperamos agregar a este sistema la implementación de las emisiones de incendios en los próximos meses.

An Integrated Web-Based System for the Monitoring and Forecasting of Coastal Harmful Algae Blooms: Application to Shenzhen City, China

Harmful algal blooms (HABs) cause environmental problems worldwide. Continuous monitoring and forecasting of harmful algal blooms are necessary for marine resources managers to detect the intensity and spatial extent of HABs and provide early warnings to the public. In this study, we introduce an integrated web-based system for the monitoring and forecasting of coastal HABs. The system is named the Harmful Algal Blooms Monitoring and Forecasting System (HMFS). HMFS integrates in situ observations, a remote-sensing-based model, hydrodynamic and water quality model and Web-Based Geographic Information System (GIS) techniques into one environment. The in situ sensors and remote sensing model provide automatic and continuous monitoring of the coastal water conditions. The numerical models provide short-term prediction and early warning of HAB of up to 5 days. The overall forecast accuracy is more than or equal to 50% for the major coastal areas of Shenzhen in 2018. By leveraging a web-based GIS technique and Service-Oriented Architecture (SOA), the web portal of HMFS provides a graphic interface for users and mangers to view real-time in situ measurements and remote sensing maps, explore numerical model forecasts and get early warning information. HMFS was applied to Shenzhen, which is a rising megacity in Southern China. The application study demonstrated the applicability and effectiveness of HMFS for monitoring and predicting HABs.

Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones

Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical forecast models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.