Performance of probabilistic forecast of the onset of the rainy season over Java Island based on the application of Constructed Analogue (CA) method on Climate Forecast System Version 2 (CFSV2) model output

The onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.

Evaluation of the Climate Forecast System Reanalysis weather data for the hydrological model in the Arctic watershed Målselv

The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were conducted by the model for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrate its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data are a reliable source for running hydrological models in the Arctic watershed Målselv.

Reanalysis intercomparison of potential vorticity and potential-vorticity-based diagnostics

Global reanalyses from data assimilation systems are among the most widely used datasets in weather and climate studies, and potential vorticity (PV) from reanalyses is invaluable for many studies of dynamical and transport processes. We assess how consistently modern reanalyses represent potential vorticity (PV) among each other, focusing not only on PV but also on process-oriented dynamical diagnostics including equivalent latitude calculated from PV and PV-based tropopause and stratospheric polar vortex characterization. In particular we assess the National Centers for Environmental Prediction Climate Forecast System Reanalysis/Climate Forecast System, version 2 (CFSR/CFSv2) reanalysis, the European Centre for Medium-Range Weather Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological Agency's 55-year (JRA-55) reanalysis, and the NASA Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). Overall, PV from all reanalyses agrees well with the reanalysis ensemble mean, providing some confidence that all of these recent reanalyses are suitable for most studies using PV-based diagnostics. Specific diagnostics where some larger differences are seen include PV-based tropopause locations in regions that have strong tropopause gradients (such as around the subtropical jets) or are sparse in high-resolution data (such as over Antarctica), and the stratospheric polar vortices during fall vortex formation and (especially) spring vortex breakup; studies of sensitive situations or regions such as these should examine PV from multiple reanalyses.

USO DE DADOS METEOROLÓGICOS DE REANÁLISE PARA A SIMULAÇÃO DA VAZÃO NA BACIA HIDROGRÁFICA DO RIBEIRÃO DO PINHAL, LIMEIRA/SP

A grande preocupação com a preservação dos recursos hídricos demanda estudos capazes de avaliar e mitigar os impactos decorrentes das ações antrópicas. O uso de modelos hidrológicos constitui-se numa ferramenta importante, pois permitem simular diferentes cenários e seus impactos na disponibilidade hídrica. No entanto, a complexidade de obter dados meteorológicos observados, torna necessária a utilização de outras fontes de dados. Objetivou-se avaliar se com a utilização de dados meteorológicos de reanálise obtidos do modelo Climate Forecast System Reanalysis (CFSR) é possível calibrar o modelo Soil and Water Assessment Tool (SWAT) e simular a produção de água numa bacia hidrográfica. A área de estudo é a bacia hidrográfica do Ribeirão do Pinhal (BHRP), situada no município de Limeira-SP. Utilizou-se o modelo SWAT para simulação hidrológica com os dados meteorológicos observados e de reanálise. Foi possível calibrar o modelo SWAT utilizando os dados meteorológicos observados e de reanálise. Porém, o ajuste foi melhor quando se utilizou os dados meteorológicos observados. A vazão simulada utilizando dados de reanálise foi superestimada. Os dados meteorológicos de reanálise são adequados para simulações hidrológicas com o modelo SWAT, o que é evidenciado por meio dos índices estatísticos satisfatórios obtidos no procedimento de calibração do modelo SWAT.