Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products.In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm (Brocca et al., 2014) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R_ASC, are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013.The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency (NS) less than 30% when using SM2R_ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R_ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas.     

Cartograms for Use in Forecasting Weather-Driven Natural Hazards

This paper evaluates the potential of using cartograms for visualizing and interpreting forecasts of weather-driven natural hazards in the context of global weather forecasting and early warning systems. The use of cartograms is intended to supplement traditional cartographic representations of the hazards in order to highlight the severity of an upcoming event. Cartogrammetric transformations are applied to forecasts of floods, heatwaves, windstorms and snowstorms taken from the European Centre for Medium-range Weather Forecasts (ECMWF) forecast archive. Key cartogram design principles in standard weather forecast visualization are tested. Optimal cartogram transformation is found to be dependent on geographical features (such as coastlines) and forecast features (such as snowstorm intensity). For highly spatially autocorrelated weather variables used in analysing several upcoming natural hazards such as 2m temperature anomaly, the visualization of the distortion provides a promising addition to standard forecast visualizations for highlighting upcoming weather-driven natural hazards.     

Event-driven flood management: design and computational modules

Flood management is a set of activities that have to be carried out in collaboration with multiple agencies. Advanced flood information with early warning generated using remote sensing satellite technologies can help the agencies to effectively manage the situation on ground. Various environmental parameters and forecasts provided by different agencies can be analyzed and compared with historical flood events for generating probable flood event alerts. The information (environmental parameters) provided by the agencies are heterogeneous and noncompliant to standards and distributed in nature. Synchronization of data from distributed resources and automation of data analysis process for flood management is a primary prerequisite for faster and efficient decision-making. Web 2.0-based web services enable data creation, sharing, communication, and collaboration on web. Spatial data sharing on web 2.0 for making quality of service using open-source software for efficient flood management is a challenge. Available software architectures proposed for risk and environmental crisis management are too generic in nature and needs lot of modification for flood management. An event-driven model coupled with data standardization procedures using service-oriented architecture provides an effective framework for flood management. In this paper, a framework capable of collecting heterogeneous distributed flood-related information for analyzing and alerting probable flood events is proposed. The framework has been implemented to generate automatic flood extent maps, by analyzing the distributed satellite data (as service). The automation of flood delineation process reduces the overall flood product generation time. Open-source web tools have been utilized in development of spatial information system to visualize and analyze the actual situation on ground facilitating overall decision-making process.     

Evaluation of satellite-derived agro-climate variables in the Northern Great Plains of the United States

The climate of the United States Northern Great Plains region is highly variable. Modelling of agriculture in this region and similar locations depends on the availability and quality of satellite and ground data for agro-climate variables. We evaluated tropical rainfall measuring mission (TRMM) multi-satellite preparation analysis (TMPA) precipitation, atmospheric infrared sounder (AIRS) surface air temperature, and AIRS relative air humidity (RH). A significant bias was found within the temperature and RH products and no bias but an insufficient rain event detection skill in the precipitation product (probability of detection ～0.3). A linear correction of the temperature product removed the bias as well as lowered the root mean square deviation (RMSD). The bias-corrections for RH led to increased RMSD or worse correlation. For precipitation, the correlation between the satellite product and ground data improved if cumulative precipitation or only precipitation during the growing season was used.     

Impact of GPS radio occultation data assimilation on regional weather predictions

The impact of GPS radio occultation (RO) data assimilation on severe weather predictions in East Asia is introduced and reviewed. Both the local observation operator that assimilates the retrieved refractivity as local point measurement, and the nonlocal observation operator that assimilates the integrated retrieved refractivity along a straight raypath have been utilized in WRF 3DVAR to improve the initial analysis of the model. A general evaluation of the impact of these approaches on Asian regional analysis and daily prediction is provided in this paper. In general, the GPS RO data assimilation may improve prediction of severe weather such as typhoons and Mei-yu systems when COSMIC data were available, ranging from several points in 2006 to a maximum of about 60 in 2007 and 2008 in this region. Based on a number of experiments, regional model predictions at 5 km resolution were not significantly influenced by different observation operators, although the nonlocal observation operator sometimes results in slightly better track forecast. These positive impacts are seen not only in typhoon track prediction but also in prediction of local heavy rainfall associated with severe weather over Taiwan. The impact of 56 GPS RO soundings on track prediction of Cyclone Gonu (2007) over the Indian Ocean is also appealing when compared to other tracks assimilated with different observations. From a successive evaluation of skill scores for real-time forecasts on Mei-yu frontal systems operationally conducted over a longer period and predictions of six typhoons in 2008, assimilation of GPS RO data appears to have some positive impact on regional weather predictions, on top of existent assimilation with all other observations.     

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.     

Using GPS multipath to measure soil moisture fluctuations: initial results

Measurements of soil moisture are important for studies of climate and weather forecasting, flood prediction, and aquifer recharge studies. Although soil moisture measurement networks exist, most are sparsely distributed and lack standardized instrumentation. Measurements of soil moisture from satellites have extremely large spatial footprints (40–60 km). A methodology is described here that uses existing networks of continuously-operating GPS receivers to measure soil moisture fluctuations. In this technique, incoming signals are reflected off and attenuated by the ground before reception by the GPS receiver. These multipath reflections directly affect signal-to-noise ratio (SNR) data routinely collected by GPS receivers, creating amplitude variations that are a function of ground reflectivity and therefore soil moisture content. After describing this technique, multipath reflection amplitudes at a GPS site in Tashkent, Uzbekistan are compared to estimates of soil moisture from the Noah land surface model. Although the GPS multipath amplitudes and the land surface model are uncalibrated, over the 70-day period studied, they both rise sharply following each rainfall event and slowly decrease over a period of ∼10 days.     

Estimation of surface runoff potential of a watershed in semi-arid environment — A case study

An accurate assessment of run-off through aerial rainfall is a basic concept in most of the rainfall-runoff models, particularly conceptual models which emphasis a complete water balance. The run-off measurements by gauging can only be regarded as an index of rainfall and restrict our ability to successfully model the rainfall-runoff process. To overcome some of these problems, remote sensing satellite data are of immense use, particularly in mountainous and desert areas. Therefore in the present study, a typical watershed from a drought hit Banswara district of Rajasthan has been analysed using IRS-1B-LISS II satellite imagery for estimating the run-off potential under different geomorphic set-up. The run-off potential was estimated using SCS method based on the satellite data in conjunction with ground truth information collected during field visit. The results indicated that the soil and water conservation measures in the watershed would improve the existing water potential and storage capacity of the study area. Based on the study eight check dams and five lift irrigation schemes are proposed.     

遥感资料在SCS模型产流计算中的应用

SCS模型在降雨径流关系上,考虑了土壤类型、土地利用和前期土壤含水量状况,使产流计算建立在较合理的基础上。但由于反映上述因素的综合指标CN的地区性、前期土壤含水量分级太粗、土壤分类的不通用性等,在我国直接应用误差太大。本文分析了误差太大的原因,提出了对SCS模型产流计算的改进方法;并采用土壤质地分类,提出用混合聚类法进行土壤分类。改进后的SCS模型产流计算不仅提高了精度,还可用来进行降雨径流预报。     

Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season

A study of the impact of FORMOSAT-3/COSMIC GPS radio occultation (RO) and dropwindsonde data on regional model simulations for a 11-day period during the 2007 Mei-yu season is presented. The Weather Research and Forecasting (WRF) model and its three-dimensional variation component, WRF-Var, are used for regional model predictions of heavy rainfall events in Taiwan. Without the use of GPS RO and dropwindsonde data, pressure and relative humidity are, in general, underestimated by the model; temperature predictions have a warm bias at the low level and a cold bias at the high level; and the east–west and north–south component winds have positive and negative biases, respectively. Incorporating GPS RO data tends to improve the prediction for longer integration. The assimilation of dropwindsonde data improves the forecast at the earlier time and at higher levels, and the improvement decreases over time. The reason the dropwindsonde data produce a positive impact earlier and the GPS RO data later is that there are few GPS RO observations in the fine domain. The large-scale simulation is first improved using the GPS RO observations, and the resulting changes can have a positive impact on the mesoscale at the later time. The dropwindsonde observations were taken inside the fine domain such that their impact can be detected early in the simulation. With both types of observation included, the prediction shows even greater improvement. At the earlier forecast time, there is nearly no impact from GPS and dropwindsonde data on rainfall forecasts. However, at the later integration time, the GPS data start to significantly improve the rainfall forecast. The dropwindsonde data also provide a positive impact on rainfall forecasts, but it is not as significant as that of the GPS data.     

Geo-limis - a decision support system for minimizing locust impact in republic of kazakhstan

Desert locusts (DL) are serious problem during April to August in the deserts and semi-deserts of Republic of Kazakhstan and causing extensive crop damage. There is no institutional and functional mechanism to forecast the habitat of locusts and most of the area remains unnoticed after laying eggs. The key to improve DL forecasting and control depends on the collection and generation of historical database on locusts, weather and habitat from affected region. Looking at the problem, a Spatial Decision Support Systems (DSS) has been developed on ARC/INFO GIS with ergonomic user interface for ingestion and subsequent analysis of locust related information vis-à-vis bio-physical and climatic data acquired from various satellite sensors and hydromet weather server respectively, to identify high frequency breeding areas well before the physiological development is completed. Weather based analytical models for physiological development of DL has been dovetailed with the SDSS for facilitating historic and present data analysis in relation to locust activity. This has augmented the surveying capability and better forecasting.     

气象卫星条件植被指数监测土壤状况

本文介绍用1985-1991年NOAA卫星标准化植被指数(NDVI)资料进行处理生成的条件植被指数(VCI),研究我国土壤的湿度状况,并阐述了应用VCI,结合常规资料进行综合分析,监测由于干旱或大范围洪涝所造成的宏观植被状况变化的情况。研究结果表明,用气象卫星资料可以对我国的干旱、洪涝状况进行宏观动态监测。     

一种基于卫星遥感的临近预报方法

中尺度对流系统(MCS)是形成强对流天气的主要原因,云团在MCS生命周期中的分裂合并问题是临近预报的难点。为解决这一问题,本文提出了FCC方法,该方法使用质心位移和FY-2卫星数据预测多个对流单体的运动轨迹。多个案例分析证明,FCC算法在MCS的各个生命周期均能进行有效的预测,包括初生、成熟和消散阶段。此外,通过列联表方法验证了所提算法的有效性。     

融机器学习与WRF大气模式的PM2.5预报方法

针对当前我国重污染天气实时的空气质量预报问题,该文提出了一种融合随机森林算法与WRF大气模式的PM2.5浓度实时预报方法。该方法结合了北京市地面空气质量监测数据和WRF气象数据进行分析,将高层大气状态(如逆温层高度等)融入了预报模型中,建立了0~72h的PM2.5浓度实时预报模型。实验证明,该模型能够对0~72h单站点的PM2.5浓度进行较高精度的实时预报,且在24~72h的长时预报结果上较基于地面空气污染物数据与地表气象站数据的预报方法精度有明显提升,即该方法可以更好地模拟大气物理化学状态,从而更为精准地进行长时PM2.5浓度预报。     

基于WebGIS的河南省实时天气监测预警系统设计

为了满足河南省气象台的业务需求,实现对实时天气及灾害性天气的监测和预报预警,提出了采用B/S架构,基于WebGIS技术开发河南省实时天气监测预警系统的设计。该系统以Visual Studio 2013为开发平台,基于Microsoft.NET Framework框架,并利用Geographic Information System(GIS)的可视化技术和空间数据的分析处理技术,实现对河南省区域气象信息的实时监测。利用该系统,用户可以进行实时天气、灾害性天气的监测和报警,强对流天气分析以及预报预警产品的制作与发布。     

应用FY－1B气象卫星监测1991年江淮洪水的研究

本文研究了ＦＹ－１ＢＶＨＲＳＲ的三个可见光通道和一个近红外通道监测１９９１年夏天中国江淮地区洪水的能力。分析该区域不同水体的各个通道的光谱状况表明，ＶＨＲＳＲ第二通道区别水、陆（植被）边界十分显著，ＶＨＲＳＲ第一通道有感应洪水泥沙含量信息的能力，ＶＨＲＳＲ的第三、四通道（海洋通道）则可以获得水深信息。文中针对ＦＹ－１Ｂ的波段的特点设计了几种信息增强方法，从而有效地识别出水陆边界，计算了洪水深度及洪水浑浊度的相对等级，精确地统计出洪水的面积。结果表明ＦＹ－１Ｂ在洪水监测中大有潜力。     

基于Google Earth的气象多参数综合显示系统

利用Google Earth平台,将天气过程中的雷电、红外云图、MICAPS高空图以及TMPA降水资料,通过编写KML/KMZ文件,建立了气象多参数综合显示系统。该系统平台的建立,可在三维空间中真实动态地再现天气的发生发展及演变过程。此平台将有利于灾害性天气成灾过程和物理机制的研究。     

Forecasting vegetation greenness with satellite and climate data

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R/sup 2/ values range from 0.97-0.80 for 2-12 week forecasts, with higher R/sup 2/ associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.     

Study of cloud liquid water path and total precipitable water from irs-p4/msmr and numerical weather prediction model output

A global weather analysis-forecast system is used to produce six hourly analysis of meteorological fields at roughly 150 km × 150 km resolution at the National Center for Medium Range Weather Forecast (NCMRWF). In this paper, we have studied the Total Precipitable Water Content (TPWC) and Cloud Liquid Water Path (CLWP) derived from the Indian Remote Sensing (IRS-P4) Satellite over the Indian Ocean region in relation to operational numerical weather prediction (NWP) model analysis and short-range forecasts. An objective analysis was carried out by introducing the observations of CLWP, TPWC and their values (six hour forecasts) from the T80 model as the first guess, for a 20 days period of August 1999 using the standard Cressman’s technique. The reanalysis could capture the signature of TPWC and CLWP data from IRS-P4 satellite. In general the observed values of TPWC and CLWP from IRS-P4 have a positive bias compared to NCMRWF analysis over the region where the satellite passed. The CLWP values have been compared with Special Sensor Microwave/Imager (SSM/I) products from the Defense Meteorological Satellite Program (DMSP) satellites. Results indicate that the model derived CLWP values were within acceptable limits, whereas the observations from the Multi-channel Scanning Microwave Radiometer (MSMR) showed slightly larger values.     

顾及降雨影响的动态优化时滞时序GM(1,2)模型在滑坡位移预测中的应用

滑坡体除了因自身重力产生位移外,还受到降雨的影响,但通常降雨对滑坡位移的作用具有滞后性。为了分析并预测降雨对滑坡位移的影响,本文提出一种顾及降雨影响的动态优化时滞时序GM(1,2)滑坡位移预测模型。首先,利用经验模态分解(EMD)分解位移序列并通过时间序列重构得到周期位移序列和趋势位移序列,对降雨数据和滑坡周期位移序列进行时滞分析和相关分析,确定时滞时间和影响程度,建立基于背景值优化的动态时滞GM(1,2)模型预测降雨量变化导致的滑坡周期位移变化;然后,建立门限自回归模型预测滑坡趋于自然变化的趋势位移;最后,通过时序叠加得到顾及降雨影响的滑坡预测位移,建立了顾及降雨因素的动态优化时滞时序GM(1,2)组合预测方法。本文以福宁高速公路八尺门滑坡和秭归县八字门滑坡监测数据为例,验证了动态优化时滞GM(1,2)模型的精度,并与其他模型的预测结果进行了对比分析。试验结果表明,动态优化时滞时序GM(1,2)组合预测模型能准确地预测降雨影响导致的滑坡位移变化,预测效果较好,该组合模型对滑坡灾害的预警与防治具有一定的参考价值。