Abstract

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

Abstract

As the new-generation geostationary satellite Himawari-8 provides a greater frequency and more observation channels than its predecessor, the Multifunctional Transport Satellite series (e.g., MTSAT-2), an opportunity arises to generate atmospheric motion vectors (AMVs) with an increased accuracy and extensive distribution over eastern Asia. In this work AMVs were derived from consecutive images of an infrared-window channel (IR1) of the Himawari-8 satellite using particle image velocimetry (PIV) based on the theory of cross-correlation schemes. A multipass scheme and an adaptive interrogation scheme were also employed to increase spatial resolution and accuracy. For height assignment, an infrared-window method was applied for opaque cloud, while an H₂O-intercept method was employed for semitransparent cloud. Validation was conducted by comparing the PIV-derived AMVs with wind fields obtained from NWP analysis, radiosonde observations, and the operational system from the Meteorological Satellite Center (MSC) of the Japan Meteorological Agency (JMA) or JMA/MSC. The comparison of wind velocity maps with the NWP data shows that the PIV-derived AMVs are capable of quantitatively depicting full-field wind field maps and strong jets in atmospheric circulation. Through comparisons with radiosonde observations, the root-mean-square error and wind speed bias (4.29 and −1.05 m s⁻¹) of the PIV-derived AMVs are comparable to, although slightly greater than, that of the NWP data (3.88 and −0.26 m s⁻¹). Based on comparison between the PIV-derived AMVs and wind fields obtained from the JMA/MSC operational system, the PIV-derived AMVs are again comparable, producing a slightly lower error but a larger wind speed bias (−1.05 vs 0.20 m s⁻¹). This also implies that a better height assignment algorithm is necessary.

Abstract

A polarimetric radar quantitative precipitation estimation to estimate rain rate (R) from specific attenuation (A) has been applied in Taiwan's operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yrs' (2016-2018) drop size distribution dataset from an operational Parsivel network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity (Z_DR) versus reflectivity (Z).

The local DSD data was also used to derive localized R(Z) and R(K_DP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic QPE combining the localized R(A), R(Z), and R(K_DP) relationships is compared to its operational counterpart and showed about 8 % reduction in normalized mean error for the Mei-Yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships, but showed higher uncertainties than in the Mei-Yu cases. The higher uncertainties in the typhoon QPE verification was likely due to the stronger winds in typhoons than in the Mei-Yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products.

Abstract

Tropical deforestation can result in substantial changes in local surface energy and water budgets, and thus in atmospheric stability. These effects may in turn yield changes in precipitation. The Maritime Continent (MC) has undergone severe deforestation during the past few decades but it has received less attention than the deforestation in the Amazon and Congo rain forests. In this study, numerical deforestation experiments are conducted with global (i.e., Community Earth System Model) and regional climate models (i.e., Regional Climate Model version 4.6) to investigate precipitation responses to MC deforestation. The results show that the deforestation in the MC region leads to increases in both surface temperature and local precipitation. Atmospheric moisture budget analysis reveals that the enhanced precipitation is associated more with the dynamic component than with the thermodynamic component of the vertical moisture advection term. Further analyses on the vertical profile of moist static energy indicate that the atmospheric instability over the deforested areas is increased as a result of anomalous moistening at approximately 800–850 hPa and anomalous warming extending from the surface to 750 hPa. This instability favors ascending air motions, which enhance low-level moisture convergence. Moreover, the vertical motion increases associated with the MC deforestation are comparable to those generated by La Niña events. These findings offer not only mechanisms to explain the local climatic responses to MC deforestation but also insights into the possible reasons for disagreements among climate models in simulating the precipitation responses.

Abstract

Many studies have demonstrated the importance of land surface schemes in climate change studies using general circulation models (GCMs). However, there have not been many studies that explore the role of land surface schemes in the context of short-range and high spatial resolution precipitation forecasts. The motivation of this study is to examine the sensitivity of simulated precipitation, and sensible and latent heat fluxes, to the use of different land surface schemes at two different spatial resolutions. The meteorological model used is the Mesoscale Compressible Community (MC2) model, and the land surface schemes are the force–restore method and the Canadian Land Surface Scheme (CLASS). Parallel runs have been performed using MC2/CLASS and MC2/force–restore at spatial resolutions of 10 and 5 km to simulate the severe precipitation case of 19–21 July 1996 in the Saguenay region of Québec, Canada. Comparisons of the simulated precipitation time series and the simulated 48-h accumulated precipitation at different spatial resolutions with rain gauges indicate that MC2/CLASS at 5-km resolution gives the best simulated precipitation. The comparison results show the model accuracy of MC2/CLASS at 10 km is comparable to the accuracy of MC2/force–restore at 5 km. The mechanism responsible for this is that CLASS represents the land surface vegetation characteristics in a more sophisticated manner than the force–restore method. Furthermore, in CLASS, each grid square is divided into a maximum of four separate subareas, and subvariations of the grid surface vegetation characteristics are taken into account. Therefore, for a grid square containing different types of vegetation, the subgrid-scale information can be used by CLASS, and the computed effective variables that are fed back to MC2 on a 10 × 10 km² grid are equivalent to computing them at a higher effective resolution than 10 km. This higher effective resolution for surface characteristics is not found in the force–restore method. The total simulated domain-averaged precipitation, and the sum of sensible and latent heat fluxes from MC2/CLASS and MC2/force–restore at different spatial resolutions, are similar. The major difference is in the partitioning of the simulated sensible and latent heat fluxes. The positioning of the simulated precipitation has been improved by using CLASS. The overall results suggest that the impact of land surface schemes is indeed significant in a short-range precipitation forecast, especially in regions with complicated vegetation variations.

Abstract

Using high-resolution hourly precipitation data and ERA5 reanalysis data, this study employs the K-means method to categorize 32 cases of warm-sector heavy rainfall events accompanied by a warm-type shear line (WSWR) along the Yangtze–Huaihe coastal region (YHCR) from April to September during 2010–17. Considering the synoptic system features of WSWR by K means, the result reveals 15 southwest type (SW-type) and 17 south-biased type (S-type) WSWR events. Composite analysis illuminates the distinct dynamic and thermodynamic features of each type. For the SW-type WSWR, the maximum value of water vapor is concentrated around 850 hPa in the lower troposphere. The YHCR is located at the intersection of the exit area of the 850-hPa synoptic low-level jet (LLJ) and the entrance area of the 600-hPa jet. The suction effects, combined with the location of YHCR on the left side of the boundary layer jet (BLJ), facilitate the triggering of local convection. Conversely, the S-type WSWR shows peak water vapor in the boundary layer. Before the onset of WSWR events, a warm, humid tongue indicated by pseudoequivalent potential temperature θ _se is present in the boundary layer, signified by substantial unstable energy. The BLJ aids mesoscale ascent on its terminus, enhancing convergence along the coastline. The BLJ also channels unstable energy and water vapor to the YHCR, causing significant rainfall. Typical case studies of both types show similar environmental backgrounds. The scale analysis shows mesoscales of dynamic field are crucial in shaping both types of WSWR, while the large-scale and meso-α-scale dynamic field facilitate the transportation of moist and warm airflow.

Abstract

The forecast uncertainty, particularly for precipitation, serves as a crucial indicator of the reliability of deterministic forecasts. Traditionally, forecast uncertainty is estimated by ensemble forecasting, which is computationally expensive since the forecast model is run multiple times with perturbations. Recently, deep learning methods have been explored to learn the statistical properties of ensemble prediction systems due to their low computational costs. However, accurately and effectively capturing the uncertainty information in precipitation forecasts remains challenging. In this study, we present a novel spatiotemporal transformer network (ST-TransNet) as an alternative approach to estimate uncertainty with ensemble spread and probabilistic forecasts, by learning from historical ensemble forecasts. ST-TransNet features a hierarchical structure for extracting multiscale features and incorporates a spatiotemporal transformer module with window-based attention to capture correlations in both spatial and temporal dimensions. Additionally, window-based attention can not only extract local precipitation patterns but also reduce computational costs. The proposed ST-TransNet is evaluated on the TIGGE ensemble forecast dataset and Global Precipitation Measurement (GPM) precipitation products. Results show that ST-TransNet outperforms both traditional and deep learning methods across various metrics. Case studies further demonstrate its ability to generate reasonable and accurate spread and probability forecasts from a single deterministic precipitation forecast. It demonstrates the capacity and efficiency of neural networks in estimating precipitation forecast uncertainty.

Abstract

The China Meteorological Administration (CMA)’s tropical cyclone (TC) database includes not only the best-track dataset but also TC-induced wind and precipitation data. This article summarizes the characteristics and key technical details of the CMA TC database. In addition to the best-track data, other phenomena that occurred with the TCs are also recorded in the dataset, such as the subcenters, extratropical transitions, outer-range severe winds associated with TCs over the South China Sea, and coastal severe winds associated with TCs landfalling in China. These data provide additional information for researchers. The TC-induced wind and precipitation data, which map the distribution of severe wind and rainfall, are also helpful for investigating the impacts of TCs. The study also considers the changing reliability of the various data sources used since the database was created and the potential causes of temporal and spatial inhomogeneities within the datasets. Because of the greater number of observations available for analysis, the CMA TC database is likely to be more accurate and complete over the offshore and land areas of China than over the open ocean. Temporal inhomogeneities were induced primarily by changes to the nature and quality of the input data, such as the development of a weather observation network in China and the use of satellite image analysis to replace the original aircraft reconnaissance data. Furthermore, technical and factitious changes, such as to the wind–pressure relationship and the satellite-derived current intensity (CI) number–intensity conversion, also led to inhomogeneities within the datasets.

Abstract

An extreme drought occurred over Southeast China (SEC) in August 2019. We demonstrate synergistic effects of midlatitude and tropical circulation on this extreme event and highlight the impacts of the coupling and locking of two cyclones at different latitudes, which are otherwise ignored. We propose the relaying roles of the Tibetan Plateau (TP) and western North Pacific in connection with the tropical convection and SEC precipitation. The equivalent-barotropic anticyclone over the TP and lower-tropospheric cyclone over the western North Pacific both resulted from the positive Indian Ocean dipole and El Niño Modoki. The equivalent-barotropic cyclone over Northeast China originated from the dispersion of Rossby waves upstream along the subtropical waveguide associated with the North Atlantic tripole sea surface temperature anomaly pattern and the Rossby wave response to the TP precipitation deficiency. Further, they jointly contributed to this drought by inducing strong northerly wind anomalies in the entire troposphere over East China. These anomalous northerly winds led to decreased warm moisture from the south and substantial sinking motions, which inhibited the occurrence of the SEC local convection and precipitation. The SEC precipitation is closely related to convection over the Maritime Continent from a climate perspective. This relationship is verified by observations, linear baroclinic model experiments, and general circulation model sensitivity experiments with and without the TP, in which precipitation anomalies over the southern TP and Philippine Sea play important bridge roles. The results will advance the prediction of the SEC extreme drought events.

Abstract

The premonsoon circulation over South Asia in May shows remarkable interannual variations, and it can modulate the onset of the Asian summer monsoon. The present study derives two dominant stratosphere–troposphere modes over the North Atlantic in April via applying principal component (PC) analysis to regional geopotential height during 1979–2015. Both of the modes reflect the North Atlantic Oscillation (NAO)-like circulation at the midtroposphere, but the circulation patterns and planetary wave activity between the two modes are quite different. The first mode represents a stratosphere–troposphere-coupled mode. Further analysis indicates that during the years with a positive phase of the first mode, the low-level anomalous southerlies over the northern Barents Sea (BS) lead to a reduction in local sea ice, which could persist into May through the ice-albedo feedback. The BS sea ice anomalies in May could generate a southeastward propagating Rossby wave train, producing an anomalous anticyclonic circulation to the west of Qinghai–Tibet Plateau. This anomalous anticyclone induces increases in local air temperature and the meridional temperature gradient, resulting in a strengthened premonsoon circulation. Given that prediction of the premonsoon circulation over South Asia remains a challenging issue, the stratosphere–troposphere-coupling modes in April provide another potential predictability source. On the other hand, ENSO has a significant effect on the South Asian premonsoon circulations in May, and can also lead to the NAO-like circulation by influencing atmospheric Rossby waves. Such an NAO-like circulation is closely related to the second mode.