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Md. Jalal Uddin, Yubin Li, Md. Yahya Tamim, Md. Babul Miah, and S. M. Shahriar Ahmed

Abstract

Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF) model to provide effective forecasts of monthly extreme rainfall indices. In addition, RF feature importance is proposed in this study to identify the most and least important features for the proposed model. This study forecasts only statistically significant extreme rainfall indices over Bangladesh including consecutive dry days (CDD), the number of heavy rain days (R10mm; rainfall ≥ 10 mm), and the number of heavy rain days (R20mm; rainfall ≥ 20 mm) within 1–3 months of lead time. The proposed model uses monthly antecedent CDD, R10mm, and R20mm including atmospheric parameters and ocean–atmospheric teleconnections, namely, convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and North Atlantic Oscillation (NAO), as the inputs to the model. Results show that the proposed model yields the best performance to forecast CDD, R10mm, and R20mm with only the antecedent of these indices as input. Ocean–atmospheric teleconnections (IOD, ENSO, and NAO) are useful for CDD forecasting, and local atmospheric parameters (CAPE, RH, and TEM) are useful for R10mm and R20mm forecasting. The results suggest that adding atmospheric parameters and ocean–atmospheric teleconnections is useful to forecast extreme rainfall indices.

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Robert S. Arthur, Timothy W. Juliano, Bianca Adler, Raghavendra Krishnamurthy, Julie K. Lundquist, Branko Kosović, and Pedro A. Jiménez

Abstract

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored.

Significance Statement

In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.

Open access
Andrew C. Winters and Curtis L. Walker

Abstract

This study utilizes a winter severity index (WSI) to characterize the impacts of High (Great) Plains winter storms during the 2006/07–2018/19 winter seasons across Nebraska and the Colorado Front Range. Winter storms are specifically defined based on the severity of their meteorological impacts and are required to influence a majority of Department of Transportation (DOT) districts within both states. Following their identification, winter storms are examined using a jet-centered framework based on the two leading modes of North Pacific jet (NPJ) and North Atlantic jet (NAJ) variability. The analysis reveals that a retracted or equatorward-shifted NPJ establishes a highly amplified flow pattern conducive to cyclogenesis over the central United States, while a poleward- or equatorward-shifted NAJ favors the development of a strongly baroclinic environment across the study region that serves as a focal region for cyclogenesis and precipitation. Composite analyses of winter storms that rank in the top 25% and bottom 25% in terms of their aggregate WSI are also performed to identify characteristics of the synoptic-scale evolution that discriminate between “high impact” and “low impact” events, respectively. High-impact events are found to feature a more amplified upper-tropospheric flow pattern over the eastern North Pacific and western United States relative to low-impact events, which subsequently favors stronger cyclogenesis over the southern plains. The integration of jet regimes with winter storm severity metrics as part of this study offers the potential to enhance impact-based decision support services and provide the weather enterprise, and its stakeholders, with critical life-saving information.

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Chia-Ying Lee, Adam H. Sobel, Suzana J. Camargo, Michael K. Tippett, and Qidong Yang

Abstract

This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

Open access
Yujie Wang, Yang Xiang, Lianchun Song, and Xin-Zhong Liang

Abstract

Determining the contribution of urbanization to extreme high-temperature events is essential to the coordinated development of Beijing, Tianjin, and Hebei (BTH). Based on the dynamic data of land-use change in every 5 years, this study uses the coupled WRF–Building Effect Parameterization/Building Energy Model (BEP/BEM) at 1-km grid spacing to quantify the contribution of BTH urbanization to the intensity and frequency of hourly extreme high-temperature events in summer. From 1990 to 2015, extreme events over Beijing and its south increased by ∼1.5°–2°C in intensity and by 50–100 h in frequency, both of which were even higher in central Beijing and Shijiazhuang. The increases of multiyear average urbanization contribution ratios to the intensity and frequency reached 3.3% and 51.6% at the 99% confidence level (p < 0.01) from 1990 to 2015, respectively. The corresponding contributions increased 1.8 and 1.2 times more significantly in the megacities (i.e., Beijing, Tianjin, and Shijiazhuang) than small and medium-sized cities. Therefore, the rapid urbanization has substantially enhanced the extreme high-temperature events in BTH. It is necessary to limit the urbanization growth rate and implement effective adaptation and mitigation strategies to sustain BTH development.

Open access
Soubhik Biswas, Savin S. Chand, Andrew J. Dowdy, Wendy Wright, Cameron Foale, Xiaohui Zhao, and Anil Deo

Abstract

Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.

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S. Irmak and M.S. Kukal

Abstract

An interaction of trends in a multitude of climate indicators dictate how agricultural production and resource use will be affected. Turkish agroecosystems have not been evaluated for climate trends, especially focusing on spatial and temporal domains relevant for agricultural production. Long-term (1981–2017) temporal trends in agriculturally relevant climate indicators [maximum (T max), minimum (T min), and mean (T avg) air temperatures, diurnal temperature range (DTR), growing degree-days (GDD), precipitation, incoming shortwave radiation (Rs), relative humidity (RH), wind speed (u 2), saturated and actual vapor pressure (es and ea), vapor pressure deficit (VPD), grass- and alfalfa-reference evapotranspiration (ETo and ETr), and aridity index (AI)] across Turkey (Turkiye) were quantified and analyzed using the NASA-POWER dataset at 0.5° × 0.5° grid cells (n = 323) for nine agricultural zones (AZs) in Turkey. At the growing-season scale, T min, T max, T avg, GDD, es, ea, VPD, Rs, precipitation, RH, and AI showed statistically significant positive trends at 100%, 76%, 100%, 100%, 94%, 98%, 22%, 83%, 33%, 10%, and 13% of Turkey’s terrestrial area, respectively. Negative trends were observed in growing-season-scale DTR, u 2, ETo, and ETr at 38%, 38%, 10%, and 18% of the total terrestrial area, respectively. At the annual scale, ETo and ETr showed increasing trends over 37% and 19% of the area, respectively. Evaporative demand showed national mean trends of −2.6 and −4.1 mm yr−1 during the growing season, respectively. Aegean AZ showed the most negative trends in growing-season ETo and ETr. The national mean magnitude in annual total precipitation (4.7 mm yr−1) was 39% greater than that in growing-season total precipitation (3.4 mm yr−1).

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Yilong Niu, Danielle Touma, Mingfang Ting, Suzana J. Camargo, and Ruishan Chen

Abstract

Tropical cyclone precipitation (TCP), accounting for some of the most extreme rainfall events, can lead to severe flooding and landslides, which often occur together as compound natural hazards during a tropical cyclone landfall. The impact due to TCP is largely associated with its intensity and spatial extent as the storm approaches landfall. Yet it is not entirely clear how TCP intensity and spatial extent vary from one tropical cyclone to another. In this study, we employ an advanced geostatistical framework to determine the TCP intensity and spatial extent along cyclone tracks for different cyclone categories, defined using the wind speed and tropical cyclone lifetime maximum intensity (LMI) at each track point (“point intensity-LMI”). The results show that when a tropical cyclone with an LMI of a supertyphoon makes landfall and has weakened to tropical storm strength it usually produces the most intense rainfall and covers the largest spatial extent. The total TCP amount estimated using the varying spatial extent helps to determine more accurately the amount of seasonal rainfall that is from tropical cyclones in China. We also determined the rainfall trend from 1951 to 2019 for TCP and found that when compared with the inland stations the historical TCP rainfall trend in those stations near the coastline of China is significantly increasing.

Significance Statement

Heavy rainfall caused by tropical cyclones has caused huge direct or indirect economic losses in the coastal areas of China. This impact is particularly significant when the rainfall intensity is high and the area of heavy rainfall is extensive. Here we investigate the rainfall intensity and spatial extent by classifying and comparing the different types of tropical cyclones impacting China with varying intensities. To do this, we group the tropical cyclone tracks of the western North Pacific Ocean during the last seven decades according to the strength of wind speed across the cyclone tracks. We found that the largest areas and heaviest intensities of rainfall occur when a supertyphoon has weakened to a tropical storm at landfall. When considering all tropical cyclones and their rainfall contribution to rainfall over land stations, we found that tropical cyclone rainfall has become heavier in most coastal areas of China.

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Dongmei Qi, Yueqing Li, Changyan Zhou, and Dan Chen

Abstract

This study aims to examine the variation of the characteristics of summer rainstorms and water vapor budget in the Sichuan Basin by using daily precipitation observation data and monthly mean ERA-Interim reanalysis data during 1979–2016. The results show that the spatial and temporal distribution of rainstorms in the Sichuan Basin is the result of the interaction between the special topography of the Sichuan Basin and different water vapor transports at low latitudes. The precipitation amount and frequency of rainstorms are mainly affected by the water vapor transports and budgets in different regions, and the intensity of rainstorms is mainly affected by the dynamic effects of regional and local topography, especially in the western and northern basin. The main reasons for the change of summer rainstorms in the Sichuan Basin include the atmospheric circulation over the key area of air–sea interaction in the tropical region, the anomalies of regional circulation, and water vapor transports in eastern China and the Sichuan Basin. A conceptual model for the summer rainstorm anomaly in the Sichuan Basin is proposed. With the establishment of consistent easterly airflow in the low-latitude tropical area (130°E–180°, 0°–10°N) and the anticyclone on its north, an anomalous southeasterly airflow and water vapor divergence maintain over eastern and southern China while an anomalous southeasterly airflow and water vapor convergence appear over the Sichuan Basin. So, more summer rainstorms occur in this region. Conversely, with the establishment of consistent westerly airflow in that same tropical area and the cyclone on its north, an anomalous easterly airflow and water vapor convergence maintain over eastern and southern China while an anomalous northeasterly airflow and water vapor divergence appear over the Sichuan Basin. So, fewer summer rainstorms occur in this region.

Significance Statement

Rainstorm change in the Sichuan Basin has significant regional characteristics. This study aims to reveal the influence of regional variation of water vapor budget on summer rainstorms in the Sichuan Basin, which provides the important basis for the forecast of rainstorm in the Sichuan Basin, as well as new comprehension for the research and application of regional response to climate change. The amount and frequency of rainstorms are mainly affected by water vapor transports and budgets in different regions, and the intensity of rainstorms is mainly affected by the dynamic action of different regional and local topography. It reveals the new mechanism of multiscale interaction between the special topography of the Sichuan Basin and different water vapor transport in low latitudes.

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Ludovic Touzé-Peiffer, Raphaela Vogel, and Nicolas Rochetin

Abstract

A new method is developed to detect cold pools from atmospheric soundings over tropical oceans and applied to sounding data from the Elucidating the Role of Cloud–Circulation Coupling in Climate (EUREC4A) field campaign, which took place south and east of Barbados in January–February 2020. The proposed method uses soundings to discriminate cold pools from their surroundings: cold pools are defined as regions where the mixed-layer height is smaller than 400 m. The method is first tested against 2D surface temperature and precipitation fields in a realistic high-resolution simulation over the western tropical Atlantic Ocean. Then, the method is applied to a dataset of 1068 atmospheric profiles from dropsondes (launched from two aircraft) and 1105 from radiosondes (launched from an array of four ships and the Barbados Cloud Observatory). We show that 7% of the EUREC4A soundings fell into cold pools. Cold-pool soundings coincide with (i) mesoscale cloud arcs and (ii) temperature drops of ∼1 K relative to the environment, along with moisture increases of ∼1 g kg−1. Furthermore, cold-pool moisture profiles exhibit a “moist layer” close to the surface, topped by a “dry layer” until the cloud base level, and followed by another moist layer in the cloud layer. In the presence of wind shear, the spreading of cold pools is favored downshear, suggesting downward momentum transport by unsaturated downdrafts. The results support the robustness of our detection method in diverse environmental conditions and its simplicity makes the method a promising tool for the characterization of cold pools, including their vertical structure. The applicability of the method to other regions and convective regimes is discussed.

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