Thomas M. Hopson

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

A 1-10-day ensemble forecasting scheme for the major river basins of Bangladesh: forecasting severe floods of 2003-07.

Abstract This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are p...

Extended-Range Probabilistic Forecasts of Ganges and Brahmaputra Floods in Bangladesh

The authors have developed a new extended-range flood forecasting system for large river basins that uses satellite data and statistically rendered probabilistic weather and climate predictions to initialize basin-scale hydrological models. The forecasting system overcomes the absence of upstreamflow data, a problem that is prevalent in the developing world. Forecasts of the Ganges and Brahmaputra discharge into Bangladesh were made in real time on 1–10-day time horizons for the period 2003–08. Serious flooding of the Brahmaputra occurred in 2004, 2007, and 2008. Detailed forecasts of the flood onset and withdrawal were made 10 days in advance for each of the flooding events with correlations at 10 days ≥0.8 and Brier scores <0.05. Extensions to 15 days show useable skill. Based on the 1–10-day forecasts of the 2007 and 2008 floods, emergency managers in Bangladesh were able to act preemptively, arrange the evacuation of populations in peril along the Brahmaputra, and minimize financial loss. The particul...

A Wind Power Forecasting System to Optimize Grid Integration

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCARs Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcels overall ability to incorporate wind energy into their power mix.

Quantifying Streamflow Forecast Skill Elasticity to Initial Condition and Climate Prediction Skill

AbstractWater resources management decisions commonly depend on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed’s initial moisture and energy conditions and to forecast future weather and climate. These sources of predictability are investigated in an idealized (i.e., perfect model) experiment using calibrated hydrologic simulation models for 424 watersheds that span the continental United States. Prior work in this area also followed an ensemble-based strategy for attributing streamflow forecast uncertainty, but focused only on two end points representing zero and perfect information about future forcings and initial conditions. This study extends the prior approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty. The sensitivities enable the calculation of flow forecast skill elasticities (i.e., derivatives) relative to skill in eithe...

Combined Use of Vegetation Density, Friction Velocity, and Solar Elevation to Parameterize the Scalar Roughness for Sensible Heat

Abstract Monin-Obukhov similarity was used to calculate sensible heat fluxes (Hc) at an array of up to 20 surface flux measurement sites on five days in 1987 and 1989 during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment by means of spatially distributed radiometric surface temperatures from an airborne platform and ground-based data. To use Monin-Obukhov similarity, a parameterization for the scalar roughness, as a function of spatially varying leaf area index (LAI) and friction velocity (u∗), was developed from a previous, simpler parameterization. LAI was found to be significant, but the range of u∗ was too small to ascertain its significance. The parameterization was found to produce sensible heat flux values that had correlations around 0.8 with the spatially distributed sensible heat flux measurements on four of the days, but on a day with high, uniform soil moisture content, the correlation was only 0.226. It is argued that the high soil moisture values...

Assessing the Ensemble Spread–Error Relationship

AbstractThe potential ability of an ensemble prediction system (EPS) to represent its own varying forecast error provides strong motivation to produce an EPS over a less expensive deterministic forecast. Traditionally, this ability has been assessed by correlating the realized forecast error with the ensembles dispersion. This paper revisits the limitations of the skill–spread correlation, but uses aspects of the correlation to introduce two metrics to assess an EPSs capacity to provide a reliable likelihood of its own error. Using a perfect EPS, skill–spread correlation is shown to be limited by its dependence on how “skill” and “spread” are defined and, perhaps most fatally, by its inability to distill the skill–spread reliability from the stability properties of the physical system being modeled. Building from this, it is argued there are two aspects of an ensembles dispersion that should be assessed. First, is there enough variability in the dispersion to justify the expense of the EPS? The factor ...

Climate Influences on Meningitis Incidence in Northwest Nigeria

AbstractNorthwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly me...

Meningitis and Climate: From Science to Practice

Meningococcal meningitis is a climate sensitive infectious disease. The regional extent of the Meningitis Belt in Africa, where the majority of epidemics occur, was originally defined by Lapeysonnie in the 1960s. A combination of climatic and environmental conditions and biological and social factors have been associated to the spatial and temporal patterns of epidemics observed since the disease first emerged in West Africa over a century ago. However, there is still a lack of knowledge and data that would allow disentangling the relative effects of the diverse risk factors upon epidemics. The Meningitis Environmental Risk Information Technologies Initiative (MERIT), a collaborative research-to-practice consortium, seeks to inform national and regional prevention and control strategies across the African Meningitis Belt through the provision of new data and tools that better determine risk factors. In particular MERIT seeks to consolidate a body of knowledge that provides evidence of the contribution of climatic and environmental factors to seasonal and year-to-year variations in meningococcal meningitis incidence at both district and national scales. Here we review recent research and practice seeking to provide useful information for the epidemic response strategy of National Ministries of Health in the Meningitis Belt of Africa. In particular the research and derived tools described in this paper have focused at “getting science into policy and practice” by engaging with practitioner communities under the umbrella of MERIT to ensure the relevance of their work to operational decision-making. We limit our focus to that of reactive vaccination for meningococcal meningitis. Important but external to our discussion is the development and implementation of the new conjugate vaccine, which specifically targets meningococcus A.

Using Weather Forecasts to Help Manage Meningitis in the West African Sahel

AbstractUnderstanding and acting on the link between weather and meningitis in the Sahel could help improve vaccine distribution and save lives. People living there know that meningitis epidemics occur in the dry season and end after the start of the rainy season. Integrating and analyzing newly available epidemiological and meteorological data quantified this relationship, showing that that the risk of meningitis epidemics climbed from a background level of 2% to a maximum risk of 25% during the dry season. These data also suggested that, of all meteorological variables, relative humidity has the strongest correlation to cases of meningitis.Weather acts alongside a complex set of environmental, social, and economic drivers, and a complementary investigation of local and regional knowledge, attitudes, and practices suggested several additional interventions to manage meningitis. These include improved awareness of early meningitis symptoms and vaccinations for farmworkers who migrate seasonally. An econom...