GEFSv12 reforecast dataset for supporting subseasonal and hydrometeorological applications

For the newly implemented Global Ensemble Forecast System version 12 (GEFSv12), a 31-year (1989-2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System version 15.1 and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–1999) and Phase 2 (2000–2019) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast data set was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500hPa geopotential height, tropical storm track, precipitation, 2-meter temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

Selection of the Gridded Temperature Dataset for Assessment of Thermal Bioclimatic Environment Changes in Amu Darya River Basin

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming (CP) and multicriteria group decision–making methods (MCGDM) to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall (MMK) test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit (CRU). Assessment of TBI trends using CPC data revealed an increase in the minimum temperature in the coldest month over the whole basin at a rate of 0.03 to 0.08\(℃\) per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2\(℃\) and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

Projeções Climáticas de Temperatura do Ar e Precipitação para o Estado de São Paulo Utilizando o Modelo Regional RegCM4

Este trabalho teve como objetivo avaliar mudanças nos valores de precipitação e temperatura médios sobre o estado de São Paulo (Brasil) e em três índices de extremos climáticos (dias secos consecutivos – CDD, extremos chuvosos – R95p e duração de ondas de calor – HWD), entre o período presente e o final do século XXI, a partir de um modelo climático regional. Foram utilizadas três simulações/projeções do Regional Climate Model (RegCM4) para o clima presente (1995-2014) e futuro (2080-2100), e a análise foi dividida nas estações de verão (DJF) e inverno (JJA). As simulações consideram o cenário mais pessimista de concentração de gases de efeito estufa na atmosfera do IPCC (RCP8.5). Dados observados do Climate Prediction Center (CPC) são utilizados para analisar a destreza das simulações no clima presente da precipitação e da temperatura do ar. No verão, as simulações superestimam a precipitação no litoral, enquanto no inverno a representam mais próxima do observado. Para a temperatura do ar, há subestimativas no litoral sul para ambas as estações do ano. No interior do estado, as temperaturas simuladas no verão são próximas ao observado, já no inverno observa-se superestimativa desta variável. Em relação aos índices climáticos, é observada pouca mudança do CDD para o verão, e um aumento para o inverno, principalmente do interior do estado, enquanto o R95p mostra sinal oposto ao CDD. O HWD apresenta uma diminuição em DJF no interior e um aumento na região litorânea para JJA. O interior de São Paulo é identificado como a região mais suscetível aos dias secos consecutivos e extremos chuvosos, enquanto as ondas de calor apresentam um sinal de aumento mais relevante no sul e faixa leste do estado, durante o inverno.

An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data

Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks.

Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model

Analyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.

Downscaling Dinâmico através do Modelo RegCM para Diferentes Inicializações Utilizando Dados do CFSv2

Resumo O objetivo deste trabalho é avaliar as previsões climáticas regionais de precipitação sobre o Brasil durante a estação do inverno de 2018 através do modelo RegCM4.7 com diferentes inicializações, tanto espacial como em 5 áreas específicas. Para realizar alertas de possíveis anomalias abaixo/acima da normal climatológica, é necessário verificar a habilidade destes modelos em prever de forma antecipada a precipitação. O modelo RegCM4.7 foi conduzido com dados do modelo Global Climate Forecast System Version 2. As destrezas das previsões foram avaliadas de forma qualitativa e quantitativa, comparando resultados com os dados do Climate Prediction Center (CPC). Os resultados mostraram que o RegCM4.7 conseguiu prever de forma coerente a precipitação com alguns meses de antecedência para o trimestre junho, julho e agosto (JJA), com menores erros sobre as regiões Nordeste e Sudeste do Brasil, onde maiores erros foram identificados sobre os subdomínios AMZ e SUL. Observou-se que as correlações das previsões foram inferiores a 0,8 durante todos os experimentos e subdomínios, exceto para região do Nordeste que apresentou os maiores valores de correlação. De maneira geral, destaca-se que o modelo foi hábil em prever a distribuição espacial de precipitação com antecedência sobre todo domínio, porém com tendência de subestimar o observado.

On the Use of Satellite Rainfall Data to Design a Dam in an Ungauged Site

The estimation of the design peak discharge is crucial for the hydrological design of hydraulic structures. A commonly used approach is to estimate the design storm through the intensity–duration–area–frequency (IDAF) curves and then use it to generate the design discharge through a hydrological model. In ungauged areas, IDAF curves and design discharges are derived throughout regionalization studies, if any exist for the area of interest, or from using the hydrological information of the closest and most similar gauged place. However, many regions around the globe remain ungauged or are very poorly gauged. In this regard, a unique opportunity is provided by satellite precipitation products developed and improved in the last decades. In this paper, we show weaknesses and potentials of satellite data and, for the first time, we evaluate their applicability for design purposes. We employ CMORPH—Climate Prediction Center MORPHing technique satellite precipitation estimates to build IDAF curves and derive the design peak discharges for the Pietrarossa dam catchment in southern Italy. Results are compared with the corresponding one provided by a regionalization study, i.e., VAPI—VAlutazione delle Piene in Italia project, usually used in Italy in ungauged areas. Results show that CMORPH performed well for the estimation of low duration and small return periods storm events, while for high return period storms, further research is still needed.

Análise da Variabilidade Temporal da Precipitação na Cidade de Florianópolis/SC

The excess or deficiency of precipitation directly affects environmental conditions, influencing society and its various economic sectors. This study analyzes the variability of precipitation in Florianópolis / SC, using the Rain Anomaly Index (RAI). To identify possible changes in the precipitation pattern, non-parametric tests are performed with a 5% significance and linear regression with a 95% confidence level in the data from the Climate Prediction Center (CPC) during the period 1979 to 2017. The results show that the most of the positive (negative) precipitation indexes correspond to the years of the warm (cold) phase of the Pacific Decadal Oscillation (PDO), and with the highest frequency of El Niño (La Niña) events. Annual time series indicate a decline in precipitation. It is also observed: (i) a small tendency to reduce rainfall in summer, autumn and winter; (ii) a significant tendency to reduce rainfall in the spring (61.4 mm in the period) and (iii) a reduction of 7.9 mm / year and a total of 308.6 mm in the period. It can be infer that rainfall totals in the months of greatest convective warming are decreasing in recent years due to the reduction in the number of rainy days or extreme rainfall.

Detecting and Tracking Coastal Precipitation in the Tropics: Methods and Insights into Multiscale Variability of Tropical Precipitation

We propose an algorithm to detect and track coastal precipitation systems and we apply it to 18 years of the high-resolution (8 km and 30 min) Climate Prediction Center CMORPH precipitation estimates in the tropics. Coastal precipitation in the Maritime Continent and Central America contributes to up to 80% of the total rainfall. It also contributes strongly to the diurnal cycle over land with the largest contribution from systems lasting between 6 and 12 h and contributions from longer-lived systems peaking later in the day. While the diurnal cycle of coastal precipitation is more intense over land in the summer hemisphere, its timing is independent of seasons over both land and ocean because the relative contributions from systems of different lifespans are insensitive to the seasonal cycle. We investigate the hypothesis that coastal precipitation is enhanced prior to the arrival of the Madden–Julian oscillation (MJO) envelope over the Maritime Continent. Our results support this hypothesis and show that, when considering only coastal precipitation, the diurnal cycle appears reinforced even earlier over islands than previously reported. We discuss the respective roles of coastal and large-scale precipitation in the propagation of the MJO over the Maritime Continent. We also document a shift in diurnal cycle with the phases of the MJO, which results from changes in the relative contributions of short-lived versus long-lived coastal systems.

Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.