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Predictability of East Asian cold surges is studied using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2 (CFSv2). Prediction skills of the CFSv2 in forecasting cold surges, their annual variation, and their physical links to large-scale atmospheric circulation patterns are examined. Results show that the climatological characteristics of the East Asian winter monsoon can be reasonably reproduced by the CFSv2. The model can well capture the frequency, intensity, and location of cold surges at a lead time of about two weeks. Obviously, fewer-than-observed cold surge days are found in the predictions when the lead time is above 14 days. The spatiotemporal evolutions of high-, mid-, and low-level circulation patterns during cold surge occurrences are all accurately indicated in the CFSv2 prediction. Except for precipitation, the other variables associated with cold surges, such as geopotential height, wind, sea level pressure, and surface air temperature, exhibit higher skills. The lead time of skillful prediction of precipitation is limited to around 1 week, with systematic wet biases over the South China Sea, the Philippine Islands, and the northwest Pacific, but dry biases over India, the Indo-China Peninsula, and most high-latitude regions. Wave train-like patterns of geopotential height and wind differ distinguishably when cold surges occur in northern and southern regions (using 35°N as the dividing line), and the CFSv2 gives a consistent prediction to these anomalous patterns. A weaker-than-observed Siberian high and weaker northerly winds over eastern China are found in the predictions especially at longer lead times. [ABSTRACT FROM AUTHOR]

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates (SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions (MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation (OI)-based merging scheme (referred as the HL-OI scheme) is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge-satellite-model merged daily precipitation analysis, called BMEP-d (Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period (2011-14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD (GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events. [ABSTRACT FROM AUTHOR]

Subseasonal forecast skills and biases of global summer monsoons are diagnosed using daily data from the hindcasts of 45-day integrations by the NCEP Climate Forecast System version 2. Predictions for subseasonal variability of zonal wind and precipitation are generally more skillful over the Asian and Australian monsoon regions than other monsoon regions. Climatologically, forecasts for the variations of dynamical monsoon indices have high skills at leads of about 2 weeks. However, apparent interannual differences exist, with high skills up to 5 weeks in exceptional cases. Comparisons for the relationships of monsoon indices with atmospheric circulation and precipitation patterns between skillful and unskillful forecasts indicate that skills for subseasonal variability of a monsoon index depend partially on the degree to which the observed variability of the index attributes to the variation of large-scale circulation. Thus, predictions are often more skillful when the index is closely linked to atmospheric circulation over a broad region than over a regional and narrow range. It is also revealed that, the subseasonal variations of biases of winds, precipitation, and surface temperature over various monsoon regions are captured by a first mode with seasonally independent biases and a second mode with apparent phase transition of biases during summer. The first mode indicates the dominance of overall weaker-than-observed summer monsoons over major monsoon regions. However, at certain stages of monsoon evolution, these underestimations are regionally offset or intensified by the time evolving biases portrayed by the second mode. This feature may be partially related to factors such as the shifts of subtropical highs and intertropical convergence zones, the reversal of biases of surface temperature over some monsoon regions, and the transition of regional circulation system. The significant geographical differences in bias growth with increasing lead time reflect the distinctions of initial memory capability of the climate system over different monsoon regions. [ABSTRACT FROM AUTHOR]

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model, version 2.0.1. The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs, all at the same forecast valid time. This lagged average forecast (LAF) method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean. Our analyses of the Equitable Threat Score, the Hanssen and Kuipers Score, and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d and 5 mm d in many regions of China, and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members. In particular, significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40%-50% in the summer season; these regions include northeastern and central to southern China, and the southeastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Biases of subseasonal prediction of the Asian summer monsoon are diagnosed using daily data from the hindcasts of 45-day integrations by the National Centers for Environmental Prediction Climate Forecast System version 2. The retrospective forecasts often show apparent systematic biases, which are mostly represented by the underestimation of the whole Asian monsoon. Biases depend not only on lead time, but also on the stage of monsoon evolution. An abrupt turning point of bias development appears around late June and early July, when ensemble spread and bias growth of winds and precipitation show a significant change over the northwestern Pacific (NWP) and the South Asian summer monsoon (SASM) region. The abrupt turning of bias development of winds, precipitation, and surface temperature is also captured by the first two modes of multivariate empirical orthogonal function analysis. Several features appear associated with the abrupt change in bias development: the western Pacific subtropical high (WPSH) begins its first northward jump and the surface temperature over the Tibetan Plateau commences a transition from warm bias to cold bias, and a reversal of surface temperature biases occurs in the eastern tropical Indian Ocean and the SASM region. The shift of WPSH position and the transition of surface thermal bias show close relationships with the formation of bias centers in winds and precipitation. The rapid growth in bias due to the strong internal atmospheric variability during short leads seems to mainly account for the weak WPSH and SASM in the model. However, at certain stages, particularly for longer-lead predictions, the biases of slowly varying components may also play an important role in bias development of winds and precipitation. [ABSTRACT FROM AUTHOR]