Search

A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

The performance of the forecasts of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecasting System (IFS) with a horizontal resolution of 0.125° is assessed through comparison with station raingauge data in the warm season (May-October) in the period 2017-2018 over southwestern China. The mean state of rainfall amount, frequency and intensity, as well as the diurnal cycle of precipitation, are involved in the evaluation. The IFS can capture well the spatial distributions of rainfall amount, frequency and intensity in the gauge data, but the rainfall frequency is generally larger and the intensity is weaker in the IFS. The rainfall events of the IFS usually start and peak much earlier, and the afternoon rainfall peaks are overestimated. The discrepancy between the gauge data and the IFS is larger over the western part of southwestern China. The results indicate that the IFS forecasts show considerable uncertainty when moving to the sub-daily scale, especially over areas with complex topography. When the IFS forecasts are applied operationally, the deviations (as revealed in this study) should be paid more attention. [ABSTRACT FROM AUTHOR]

Dual‐frequency radar (DFR) could improve the accuracy of estimating the microphysical properties of rain profiles. Unfortunately, factors that cause inaccuracies in retrieved rain profiles include uncertainties in raindrop size distribution (DSD) parameterizations, DFR retrieval methods and radar measurement errors. The primary objective of this study was to assess the uncertainties in retrieving rain profiles to offer insight into application considerations for ground‐based DFRs in China. The uncertainties caused by DSD models are assessed by comparing attenuation coefficients and rain rates from DFR algorithms with those directly derived from DSD spectra from disdrometers. Then, based on a DSD model, the impacts of retrieval methods, radar range resolution, measurement error and errors in temperature on DFR retrievals are explored by comparing the rain profiles obtained using DFR algorithms with those directly output from the Weather Research and Forecasting model. Overall, the impact of measurement error on the DFR retrievals is relatively significant for both the forward retrieval method and the iterative backward retrieval method and should be eliminated in practical applications. At an individual uncertainty level, the impact of DSD parameterizations on the retrievals from the dual‐frequency technique is less than ±10% bias when the shape factor μ of the gamma distribution ranges from 2 to 4. The retrieved rain profiles from the forward retrieval method are relatively sensitive to radar range resolution and the retrieval performance can be improved with increasing the range resolution, while the iterative backward retrieval method exhibits stable retrieval performance. The impact of temperature errors on the retrieved rain profiles is negligible. [ABSTRACT FROM AUTHOR]

The goal of this study was to improve the accuracy of model forecasting, such that forecasters could use model products to make more efficient daily weather predictions. Historical data of the 12 hr following a given time for various meteorological factors from the control forecasts of the European Centre for Medium‐Range Weather Forecasting (ECMWF) between 20 ° and 40 ° N latitude and 110 °–130 ° E longitude were used to verify the performance of the proposed method. Eight major meteorological factors were selected via correlation analysis between control forecast meteorological factors and real‐time rainfall. The samples were divided into four types using the K‐means clustered method. Each type was respectively modelled by long short‐term memory (LSTM) in order to correct rainfall forecasts for eastern China. The eight major meteorological factors were used as the model input, and the differences between real‐time rainfall data and model‐forecast rainfall were used as the model output. The corrected results revealed that the root mean square error decreased by 0.65, and the threat scores of light rainfall and rainstorms were improved. [ABSTRACT FROM AUTHOR]

Results from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs. [ABSTRACT FROM AUTHOR]