•Three precipitation products are assessed using the Bayesian uncertainty analysis.•Precipitation uncertainty and model errors are modeled jointly with a new error model.•Using multi-satellite ...precipitation ensemble with BMA improves predictive performance.
Global satellite–gauge merged precipitation (SGMP) products combine the advantages of satellite precipitation estimates with rain gauge data, providing great potential to hydrological applications. However, the inaccuracies of the precipitation products together with hydrologic model limitations, could cause great uncertainty in streamflow predictions. Therefore, this study investigates the hydrological value of three mainstream global SGMP products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7 product, the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD), the Global Satellite Mapping of Precipitation (GSMaP) Gauge-calibrated product (GSMaP Gauge). They are used as the precipitation input of the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin in China. To better quantify their effects on parameter calibration and streamflow predictions, a newly developed residual error model accompanied with the Bayesian uncertainty analysis are performed. CMORPH satellite-gauge merged precipitation product, recently developed by the China Meteorological Administration (CMA) (CMORPH CMA), is a high-quality regional precipitation product. Thus, this study applies the CMORPH CMA within the same framework to provide a benchmark. The results show that the parameter uncertainty are influenced significantly by the input of various precipitation products. There is a tradeoff between the deterministic streamflow performance and the probabilistic predictive performance for selecting the best input among the three global precipitation products. The streamflow uncertainty intervals of the three global precipitation products are then merged using the Bayesian Model Averaging (BMA) method. The BMA results show satisfying hydrological performance in terms of deterministic streamflow predictions, with the largest Nash-Sutcliffe coefficient of Efficiency (NSCE) values of 0.86 and 0.64, and the smallest absolute relative error (RE) values of 0% and 10.2% in the calibration and validation periods, respectively. In addition, the BMA results also produce much more reliable probabilistic predictions, which even outperform the outcomes of the high-quality CMORPH CMA. Our study demonstrates the potential uncertainty of various SGMP products for model calibration and streamflow predictions. The hydrologic ensemble using multiple global SGMP products provides a promising and advantageous approach to support water management and decision making, especially in ungauged basins.
This study investigates what can be the response of East Asian summer rainfall (EASR) if global warming causes more extreme El Niño events in the future. Two multi-model ensembles are built based on ...CMIP6 models that project stronger El Niño-Southern Oscillation (ENSO) in the future and CMIP6 models that project no changes in ENSO, respectively. Since the ENSO impact in the EASR is influenced by the Pacific Decadal Oscillation (PDO) phase, the analysis separates extreme El Niño events occurring in different PDO phases. During the negative PDO phase, CMIP6 models that project stronger El Niño events also project enhanced EASR anomalies compared with models that project similar El Niño magnitudes to the present. However, during the positive PDO phase, the difference in the magnitude of the EASR changes between the two ensembles is negligible. To understand which components of the ENSO future changes are influencing the EASR changes, an atmospheric moisture budget decomposition is applied. The results indicate that future changes in ENSO-driven wind circulation anomalies are the major contributor to EASR changes.
•CMIP6 models that project stronger El Niño events project enhanced EASR changes.•EASR changes mainly occur in extreme El Niño events during the negative PDO phase.•EASR changes are due to changes in ENSO-driven wind circulation anomalies.
Ozone pollution in China is influenced by meteorological processes on
multiple scales. Using regression analysis and weather classification, we
statistically assess the impacts of local and synoptic ...meteorology on daily
variability in surface ozone in eastern China in summer during 2013–2018. In
this period, summertime surface ozone in eastern China (20–42∘ N, 110–130∘ E) is among the highest in the world, with regional means of 73.1
and 114.7 µg m−3, respectively, in daily mean and daily maximum
8 h average. Through developing a multiple linear regression (MLR) model
driven by local and synoptic weather factors, we establish a quantitative
linkage between the daily mean ozone concentrations and meteorology in the
study region. The meteorology described by the MLR can explain
∼43 % of the daily variability in summertime surface ozone
across eastern China. Among local meteorological factors, relative humidity
is the most influential variable in the center and south of eastern China,
including the Yangtze River Delta and the Pearl River Delta regions, while
temperature is the most influential variable in the north, covering the
Beijing–Tianjin–Hebei region. To further examine the synoptic influence of
weather conditions explicitly, six predominant synoptic weather patterns
(SWPs) over eastern China in summer are objectively identified using the
self-organizing map clustering technique. The six SWPs are formed under the
integral influence of the East Asian summer monsoon, the western Pacific
subtropical high, the Meiyu front, and the typhoon activities. On average,
regionally, two SWPs bring about positive ozone anomalies (1.1 µg m−3
or 1.7 % and 2.7 µg m−3 or 4.6 %), when eastern China is under a weak cyclone system or under the prevailing
southerly wind. The impact of SWPs on the daily variability in surface ozone
varies largely within eastern China. The maximum impact can reach ±8 µg m−3 or ±16 % of the daily mean in some areas. A combination of the regression and the clustering approaches suggests a strong performance of the MLR in predicting the sensitivity of surface ozone in eastern China to the variation of synoptic weather. Our assessment highlights the importance of meteorology in modulating ozone pollution over China.
Heavy rainfall occurred at the inland frontal zone and coastal warm sector in South China on 12–13 June 2019. Three convection-permitting ensemble forecast (CPEF) experiments with perturbed initial ...conditions (ICs) and lateral boundary conditions (BCs) as well as model physical schemes have been conducted to predict the double-rainbelt process. This study evaluates the predictability of different CPEF experiments and analyzes the causes of precipitation forecast errors by identifying the sensitive factors for frontal rainfall (FR) and warm-sector rainfall (WR). In this double-rainbelt event, the forecast skill of FR is significantly better than that of WR. The experiment with perturbed ICs/BCs combining suitable model physical schemes performs best. Based on the optimal ensemble experiment, the ensemble sensitivity analysis has been conducted for the two rainbelts. FR is sensitive to the synoptic forcing and its sensitive area mainly locates near the frontal system, while the coastal boundary layer jet and water vapor from the South China Sea play an important role in the WR process. The ICs and BCs greatly influence the location of FR through varying the movement direction and velocity of the fronts. The physical schemes have a great impact on convection triggering along the coasts, thus affecting the occurrence of the whole WR process, of which the choice of microphysical scheme is critical.
•The initial and lateral boundary conditions mainly impact the distribution of frontal rainfall in the inland.•The physical schemes are critical and sensitive to convection triggering of warm-sector rainfall along the coastal areas.•Ensemble system with perturbed initial and lateral boundary conditions based on suitable physical schemes performs best.
This study pioneers the development of short-range (0-12 h) probabilistic quantitative precipitation forecasts (PQPFs) in Taiwan and aims to produce the PQPFs from time-lagged multimodel ensembles ...using the Local Analysis and Prediction System (LAPS). By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. Since LAPS adopts diabatic data assimilation, it is utilized to mitigate the "spinup" problem and produce more accurate precipitation forecasts during the early prediction stage (0-6 h). The LAPS ensemble prediction system (EPS) has a good spread-skill relationship and good discriminating ability. Therefore, though it is obviously wet biased, the forecast biases can be corrected to improve the skill of PQPFs through a linear regression (LR) calibration procedure. Sensitivity experiments for two important factors affecting calibration results are also conducted: the experiments on different training samples and the experiments on the accuracy of observation data. The first point reveals that the calibration results vary with training samples. Based on the statistical viewpoint, there should be enough samples for an effective calibration. Nevertheless, adopting more training samples does not necessarily produce better calibration results. It is essential to adopt training samples with similar forecast biases as validation samples to achieve better calibration results. The second factor indicates that as a result of the inconsistency of observation data accuracy in the sea and land areas, only separate calibration for these two areas can ensure better calibration results of the PQPFs.
Abstract
A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts ...from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.
The heaviest rainfall over 61 yr hit Beijing during 21–22 July 2012. Characterized by great rainfall amount and intensity, wide range, and high impact, this record-breaking heavy rainfall caused ...dozens of deaths and extensive damage. Despite favorable synoptic conditions, operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time. To gain a better understanding of the performance of mesoscale models, verification of high-resolution forecasts and analyses from the WRF-based BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out. The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area. Moreover, model forecasts are first verified statistically using equitable threat score and BIAS score. The BJ-RUCv2.0 forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation. Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation (> 5 mm h
−1
) are due to inaccurate precipitation location and pattern, while forecast errors for heavy rainfall (> 20 mm h
−1
) mainly come from precipitation intensity. Finally, the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters (water vapor flux and instability conditions) of the BJ-RUCv2.0 model output.
Long non-coding RNA (lncRNA) is an important member of non-coding RNA family and emerging evidence has indicated that it plays a pivotal role in many physiological and pathological processes. The ...lncRNA X inactive specific transcript (XIST) is a potential tumour suppressor in some types of cancers. However, the expression and function of XIST in breast cancer remain largely unclear. The objective of this study was to evaluate the expression and biological role of XIST in breast cancer. The results showed that XIST was significantly down-regulated in breast cancer tissues and cell lines. Further functional analysis indicated that overexpression of XIST remarkably inhibited breast cancer cell growth, migration, and invasion. The results of luciferase reporter assays verified that miR-155 was a direct target of XIST in breast cancer. Moreover, caudal-type homeobox 1 (CDX1) was identified as a direct target of miR-155 and miR-155/CDX1 rescued the effects of XIST in breast cancer cells. Taken together, our results suggest that XIST is down-regulated in breast cancer and suppresses breast cancer cell growth, migration, and invasion via the miR-155/CDX1 axis.
•LncRNA XIST was significantly down-regulated in breast cancer.•LncRNA XIST depressed the growth, migration and invasion of breast cancer.•MiR-155 was a direct target of XIST.•LncRNA XIST exerted its function via miR-155/CDX1 axis.
Verification of precipitation is one of the major issues in evaluating numerical weather prediction. In this study, a recently developed neighbourhood‐based method in terms of agreement scales is ...applied to characterize the scale‐dependent spatial spread‐skill relationship of precipitation forecasts in a 3 km convection‐allowing ensemble prediction system (EPS) over the Yangtze‐Huaihe river basin of China. Thirty cases during the Meiyu season of 2013 are classified into two weather regimes, large coverage (LC) and small coverage (SC), based on the precipitation fractional coverage. Overall, precipitation distributions for these two weather regimes are reasonably forecast by the EPS. The results show that the spatial spread‐skill relationship depends highly on the weather regime. The spatial spread‐skill relationship under SC is poorer and shows more diurnal variation compared to that under LC. In addition, this article extends the neighbourhood‐based method to investigate the relative influence of precipitation intensity and placement on the spatial spread‐skill relationship. With increasing precipitation threshold, the relative impact of precipitation intensity on the relationship gradually decreases, and the influence of precipitation placement becomes dominant.
Domain of the convective‐scale EPS with coastlines, province boundaries and topography heights, and the verification domain of precipitation forecasts (inside thick solid lines).
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