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The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

Agriculture and hydrological management in western and northwestern Iran is highly sensitive to precipitation associated with Mediterranean and Sudanese cyclones particularly during spring and winter. Although many studies have focused on the theoretical basis of these cyclones, statistical analyses, especially with satellite observations, have not been undertaken. In this study, 40 cyclones during all months of 2015–2021 are characterized using data collected by Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and ERA5 reanalysis within each cyclone quadrant. The results show that about 45% of cyclones have a lifetime between 1 and 2 days, and approximately 25% occur during April with a secondary peak in March (22.5%). Accordingly, cyclonic rainy days and rainfall amounts peak in April with values of 76.1% and 64.1%, respectively. The lowest (most negative) low‐ and mid‐tropospheric vertical velocity in the southeast and northeast quadrants indicates upward motion, which leads to the most intense latent heat release within the cyclone. Accordingly, the highest rainfall rates occur in the southeast and northeast quadrants, located within and ahead of the highest specific humidity quadrant (southeast) where the warm conveyor belt plays a significant role for rainfall formation. Maximum stratiform and convective rainfall rates are observed in the southwest and southeast quadrants, respectively. The distribution of rainfall rate is consistent with that of the storm‐top height pattern in all quadrants. The maximum percentage of warm rain to the total rain (34.6% on average) for both stratiform and convective precipitation falls in the southeast quadrant. The structure of these analysed cyclones is largely similar to that shown previously in the literature. According to the analysed rainfall in different life cycles, it is expected that the cyclones have the greatest impact (i.e., extreme rainfall) during the developing stage in the northeast and southeast quadrants, and during the mature stage in the southwest quadrant. The lowest influence is during the dissipating stage. The results from this study can help decision makers within the hydrological and risk management industries. [ABSTRACT FROM AUTHOR]

A seasonal–diurnal analysis of land-sea contrast in lightning activity over Bulgaria and the Black Sea over 10 years is presented here. The maximum number of flashes over both surface types is registered during the summer (with a peak over Bulgaria in June and over the Black Sea in July) and a minimum number in winter (December/February, respectively). During spring, the maximum flash density is observed over Bulgaria (in May), while in autumn, it is over the Black Sea (in September). The results show that only in autumn lightning activity dominates over the Black Sea compared to over land (Bulgaria), while in winter, spring, and summer is vice versa. For this reason, an additional investigation was conducted to determine whether there is a relationship between lightning activity and the sea surface temperature (SST) of the Black Sea in autumn. The analysis reveals that the influence of SST on the formation of thunderstorms over the Black Sea varies depending on the diurnal time interval, with the effect being more significant at night. At nighttime intervals, there is a clear trend of increasing mean flash frequency per case with rising SST (linear correlation coefficients range from R = 0.92 to 0.98), while during the daytime, this trend is not as evident. This indicates that, during the day, other favorable atmospheric processes have a greater influence on the formation of thunderstorms than sea-surface temperature, while in the autumn night hours, the higher SST values probably play a more significant role in thunderstorms formation, in combination with the corresponding orographic conditions. [ABSTRACT FROM AUTHOR]

Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) precipitation rate product, E-OBS ground-based precipitation gauge data, European climatological gauge-adjusted radar precipitation dataset (EURADCLIM), Operational Programme for the Exchange of Weather Radar Information (OPERA) ground-based radar-derived precipitation rates, and fifth major global reanalysis produced by ECMWF (ERA5) total and convective precipitation products. Arrival Time Difference Network (ATDnet) lightning data are used in conjunction with IMERG and EURADCLIM precipitation rates, with an imposed threshold of 10 mm h−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1%–10% of the total precipitation accumulated is classified as convective, increasing to 10%–40% over the surrounding seas. In contrast, 30%–50% of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40%–60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm h−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains. [ABSTRACT FROM AUTHOR]

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models. [ABSTRACT FROM AUTHOR]

In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. [ABSTRACT FROM AUTHOR]

A 5-yr climatology and composite study of precipitation bands associated with extratropical cyclones over the British Isles from April 2017 to March 2022 is constructed. A total of 249 single bands were manually identified from radar network mosaics in association with 167 cyclones identified from surface maps. More bands formed over water near the coast than over inland areas, and most had a meridional orientation. The average lengths of bands at the times of formation and maximum length were 290 and 460 km, respectively; only 20% of bands reached a maximum length exceeding 600 km. The number of bands decreased with increasing duration, with 31% of bands lasting for 2–3 h, with bands lasting more than 10 h uncommon. The bands were classified into six categories, with occluded-frontal bands (19 yr−1), warm-frontal bands (11 yr−1), and cold-frontal bands (10 yr−1) being the most frequent. Occluded-frontal and warm-frontal bands commonly occurred west of Scotland and in the east quadrant relative to their parent cyclones. In contrast, cold-frontal bands commonly occurred southwest of Great Britain and in the south quadrant relative to their parent cyclones. Composites for northwest–southeast occluded-frontal and warm-frontal bands west of Scotland, and southwest–northeast cold-frontal bands southwest of Great Britain, show the different synoptic environments that favor bands. The low-level jet transports moisture into the band and is similar to the location and scale of the composite bands, similar to that of an atmospheric river. These results are compared to previous studies on bands from the United States. Significance Statement: Precipitation bands are lines of heavy precipitation as seen on weather radar. Most studies of bands in extratropical cyclones have occurred in the United States. We examine 5 years of bands in extratropical cyclones over the British Isles to better understand their characteristics. Bands form in preferred geographic regions: offshore of the west coasts of Scotland, Wales, and southwest England. The most common bands are associated with occluded fronts (37% of all bands). The average scale of the bands is associated with the average scale of wind maxima 1–2 km above ground. These results provide a better understanding of the typical characteristics and conditions under which bands form and their geographical variability compared to the United States. [ABSTRACT FROM AUTHOR]

Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. Significant Statement: This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs. [ABSTRACT FROM AUTHOR]

Greenland rainfall has come into focus as a climate change indicator and from a variety of emerging cryospheric impacts. This study first evaluates rainfall in five state‐of‐the‐art numerical prediction systems (NPSs) (CARRA, ERA5, NHM‐SMAP, RACMO, MAR) using in situ rainfall data from two regions spanning from land onto the ice sheet. The new EU Copernicus Climate Change Service (C3S) Arctic Regional ReAnalysis (CARRA), with a relatively fine (2.5 km) horizontal grid spacing and extensive within‐model‐domain observational initialization, has the lowest average bias and highest explained variance relative to the field data. ERA5 inland wet bias versus CARRA is consistent with the field data and other research and is presumably due to more ERA5 topographic smoothing. A CARRA climatology 1991–2021 has rainfall increasing by more than one‐third for the ice sheet and its peripheral ice masses. CARRA and in situ data illuminate extreme (above 300 mm per day) local rainfall episodes. A detailed examination CARRA data reveals the interplay of mass conservation that splits flow around southern Greenland and condensational buoyancy generation that maintains along‐flow updraft 'rapids' 2 km above sea level, which produce rain bands within an atmospheric river interacting with Greenland. CARRA resolves gravity wave oscillations that initiate as a result of buoyancy offshore, which then amplify from terrain‐forced uplift. In a detailed case study, CARRA resolves orographic intensification of rainfall by up to a factor of four, which is consistent with the field data. [ABSTRACT FROM AUTHOR]

The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere. Significance Statement: Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology. [ABSTRACT FROM AUTHOR]

The airborne field campaign HALO–(AC)³ took place from 07 March to 12 April 2022 and was designed to observe the transformation of air masses during their meridional transport in the North Atlantic sector of the Arctic. We evaluate the meteorological and sea ice conditions during the campaign based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and atmospheric soundings with respect to climatology and describe special synoptic events. HALO–(AC)³ started with a warm period (11–20 March) where strong southerly winds prevailed that caused moist and warm air intrusions (MWAIs). Two MWAIs were detected as Atmospheric Rivers (ARs). Compared to the ERA5 climatology (1979–2022), record breaking vertically integrated poleward heat and moisture fluxes averaged over 75.0–81.5° N were found. The related warm and moist air masses reached the central Arctic, causing the highest rainfall rates over the sea ice northwest of Svalbard recorded since the beginning of the ERA5 climatology. Subsequently, the cold period of HALO–(AC)³ started after the passage of a Shapiro–Keyser cyclone on 21 March when the wind regime turned to northerlies, advecting colder air into the Fram Strait. Until 08 April, marine cold air outbreaks (MCAOs) prevailed, including two strong MCAO events on 21–26 March and 01–02 April. In between, aged subpolar warm air was advected to the Fram Strait with northeasterly and easterly winds. On average, the campaign period was warmer than the climatology, especially due to the exceptionally strong ARs during the warm period. In the Fram Strait, the sea ice concentration (SIC) was within the 10–90th percentiles of the climatology over the entire campaign duration. During the warm period, SIC was strongly reduced and an untypically large polynya for this season opened north of Svalbard. During the cold period, the polynya was closed again and above average SICs were found. We describe the environmental conditions of a Polar Low and far north reaching cirrus in detail, which will be subjects of research using HALO's remote sensing suite in the future. We also present the perspective on the HALO–(AC)³ weather conditions from the research site Ny–Ålesund, where orographic effects caused by the Svalbard archipelago and temporal shifts of atmospheric signals due to the propagation of synoptic systems must be taken into account. Overall, our study may serve as basis for future analyses of the data collected during HALO–(AC)³ and to compare synoptic conditions with other field campaigns. [ABSTRACT FROM AUTHOR]

Rapid intensification (RI) of a tropical cyclone (TC) is defined as an increase in the wind speed by 30 knots within 24 hours. It is a less understood process, particularly for TCs over the North Indian Ocean. We considered data for the past 39 years (1982–2020) from the JTWC and studied the climatology of TCs. We found that, out of 197 cases, 44 TCs (22%) experienced RI. There is a significant increasing trend in RI TCs frequency and duration over the Arabian Sea (AS). Lifetime maximum intensity (LMI) is significantly higher, and genesis to LMI duration is also significantly longer for TCs undergoing RI over the AS. LMI is double for RI TC than for non‐RI (NRI) TC. The longer duration to achieve the higher maximum intensity resulted in a longer total lifespan for RI TCs. All TCs reaching a super cyclonic storm category underwent RI and generally, RI onset happens in the depression stage. The composites are prepared to understand the characteristics of RI TCs in contrast to NRI TCs. For the RI TC composite, the period includes 12 hours before and 24 hours after the RI onset. For the NRI TC, we considered 36 hours starting from the initial intensification stage. The analysis shows the RI TCs are embedded in the warm water regions with ocean heat content of 90–100 kJ · cm−2 and strong latent heat flux transport. RI TCs also have higher positive low‐level relative vorticity, higher upper‐level divergence, and higher mid‐level relative humidity, with higher moisture content around the TC centre. Deep clouds with 208 K < IRBT <240 K dominate in RI TCs. The total surface rainfall for RI cases is symmetric around the centre. RI TCs have higher rainfall fractions with intense surface rain rates for stratiform and convective rainfall during the pre‐monsoon. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth's hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. Plain Language Summary: Atmospheric rivers (ARs) are long and narrow weather features often referred to as "rivers in the sky." They often transport water from lower latitudes to higher latitudes typically across climate zones and produce precipitation necessary for local climates. Understanding ARs in a warming climate is challenging because of the variety of ways an AR can be defined on gridded data sets. Unlike weather features such as tropical cyclones where identification methodologies are similar, algorithms that determine the characteristics of ARs vary depending on the science question posed. Because there is no real consensus on AR identification methodology, we aim to quantify the algorithmic uncertainty in AR metrics and precipitation. We compare 16 different ways of defining an AR on gridded data sets and present the range of possibilities in which an AR could change under global warming. Generally, ARs are projected to increase but the amount of that increase is a function of the algorithm. Across all algorithms and focus regions, AR precipitation is projected to become more extreme. Key Points: High‐resolution historical and future simulations are used to evaluate atmospheric river detection tools (ARDT) uncertaintyARDTs mostly show increases in frequency and intensity of future atmospheric rivers (ARs) but the scale of response is dependent on algorithmic restrictivenessMost regions experience an increase in precipitation volume coming from extreme ARs [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts. Plain Language Summary: Tropical cyclones (TCs) threaten coastlines with their extreme wind, rainfall, and storm surges. To model historical TCs and their hazards, accurate storm size information is needed. However, size information is only available from 1988‐onward, preventing accurate modeling of earlier TCs. Here, we utilize reanalysis data and a physics‐based TC wind model to reconstruct TC sizes for North Atlantic storms from 1950‐present. We then utilize a hydrodynamic ocean model to reconstruct historical TC storm surges across the US Atlantic and Gulf coasts from 1950‐present. We demonstrate that our expanded historical storm surge reconstructions can enhance understanding of coastal risk. Furthermore, our reconstructed TC sizes can facilitate modeling of historical TC wind and rainfall hazard for pre‐1988 storms. Key Points: We leverage ERA5 reanalysis data combined with a physics‐based wind model to estimate tropical cyclone (TC) storm sizeWe develop a data set of North Atlantic TC storm sizes (i.e., radius to maximum wind and outer size) from 1950 to 2020Using reconstructed TC sizes and a hydrodynamic model, we develop a data set of historical storm tides from 1950 to 2020 [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z=a⋅LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z –LWC relation for a continuous cloud system is complicated. Additional information such as liquid water path (LWP) from a co-located microwave radiometer (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln⁡a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln⁡a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln⁡a) changes for each cloud profile, and the range of ln⁡a is consistent with suggested values in the literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (southwest of France, October 2019 to March 2020), in situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud–fog heterogeneity played a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for the scaling factor are proposed for fog and clouds. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of the scaling factor as a priori information to estimate the LWC of warm clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor. [ABSTRACT FROM AUTHOR]

An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

Heavy rainfall is a challenge to forecast due to the variety of rainfall intensities and durations across a wide spectrum of high‐impact storm types. In this study, we analyze extreme storms in Tropical and Subtropical East Asia, a moisture‐rich environment with complex terrain and oceanic regions. The Tropical Rainfall Measuring Mission's Precipitation Radar is utilized to characterize the frequency and rainfall intensity of four extreme storm types. Extreme storms producing heavy precipitation are categorized into four types: deep convective cores (DCCs), deepwide convective cores (DWCCs), wide convective cores (WCCs), and broad stratiforms regions (BSRs). DCCs and DWCCs occur more frequently and produce stronger rain intensities over land compared to those over ocean. However, WCCs and BSRs occur more frequently over oceans, especially in association with the Meiyu front season and climatological progression in the northern subregions. Although the Convective Cores show higher rain intensities than the BSRs, they show lower volumetric rain rate due to their comparatively smaller horizontal area. An ingredients‐based framework is applied to find key similarities across the different heavy rainfall‐producing storms near Taiwan using ERA5 reanalysis. The analysis shows that the broader systems (i.e., WCCs and BSRs) are associated with larger in area and longer timescales of vertical moisture flux and low‐level wind shear that support the development of the horizontally large, organized storms. Smaller DCCs do not show strong vertical moisture flux on the spatial scales resolved by the reanalysis, suggesting their more local nature and less meso‐ or synoptic scale support. Plain Language Summary: Heavy rainfall is a challenge to forecast due to the variety of rainfall intensities and durations across a wide spectrum of high‐impact storm types. In this study, we analyze extreme storms in Tropical and Subtropical East Asia (TSEA), a moisture‐rich environment with complex terrain and oceanic regions. Observations from the first satellite precipitation radar are used to characterize the nature of extreme storms. Results show that deep convective storms occur more frequently over land, while the wide convective storms and the broad stratiform regions occur more frequently over oceans, especially in association with the Meiyu front (pre‐East Asian monsoon season) in northern TSEA. Broad stratiform regions can produce a larger total rainfall amount compared to the convective rain categories because of larger areas. A goal of this study is to identify similar key ingredients across different extreme storm types near Taiwan. Analysis shows that the broader storms are associated with moisture in larger areas and on longer timescales, and also low‐level wind shear that supports the development of the large, organized storms. Smaller deep convective storms do not show strong moisture signals in coarse‐scale environmental analysis, suggesting that this storm type might need more small‐scale moisture support. Key Points: The spatio‐temporal and rain frequency of extreme storm types in Tropical and Subtropical East Asia is documentedBroad stratiform environments, for example, in Meiyu Front, require a longer and larger moisture build‐upSmaller convective systems require spatially smaller diurnal vertical moisture flux and convergence [ABSTRACT FROM AUTHOR]

Multi-platform satellite-based precipitation gridded estimates are becoming widely available in support of climate monitoring and climate science. The characterization of the performances of these emerging Level-4 products is an active field of research. This study introduced a simple Gaussian mixture model (GMM) to characterize the distribution of uncertainty in these satellite products. The following three types of uncertainty were analyzed: constellation changes-induced uncertainties, sampling uncertainties and comparison with rain-gauges. The GMM was systematically compared with a single Gaussian approach and shown to perform well for the variety of uncertainties under consideration regardless of the precipitation levels. Additionally, GMM has also been demonstrated to be effective in evaluating the impact of Level-2 PMW rain estimates' detection threshold definition on the constellation changes-induced uncertainty characteristics at Level-4. This simple additive perspective opens future avenues for better understanding error propagation from Level-2 to Level-4. [ABSTRACT FROM AUTHOR]

To understand and manage water systems under a changing climate and meet an increasing demand for water, a quantitative understanding of precipitation is most important in coastal regions. The capabilities of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B product for precipitation quantification are examined over three coastal regions of the United States: the West Coast, the Gulf of Mexico, and the East Coast, all of which are characterized by different topographies and precipitation climatologies. A novel uncertainty analysis of IMERG is proposed that considers environmental and physical parameters such as elevation and distance to the coastline. The IMERG performance is traced back to its components, i.e., passive microwave (PMW), infrared (IR), and morphing-based estimates. The analysis is performed using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference at the native resolution of IMERG of 30 min and 0.1°. IMERG Final (IM-F) quantification performance heavily depends on the respective contribution of PMW, IR, and morph components. IM-F and its components overestimate the contribution of light rainfall (<1 mm h−1) and underestimate the contribution of high rainfall rates (>10 mm h−1) to the total rainfall volume. Strong regional dependencies are highlighted, especially over the West Coast, where the proximity of complex terrain to the coastline challenges precipitation estimates. Other major drivers are the distance from the coastline, elevation, and precipitation types, especially over the land and coast surface types, that highlight the impact of precipitation regimes. [ABSTRACT FROM AUTHOR]

Experiment outputs are now available from the Coupled Model Intercomparison Project's sixth phase (CMIP6) and the past climate experiments defined in the Palaeoclimate Modelling Intercomparison Project's fourth phase (PMIP4). All of this output is freely available from the Earth System Grid Federation (ESGF). Yet there is overhead in analysing this resource that may prove complicated or prohibitive. Here we document the steps taken by ourselves to produce ensemble analyses covering past and future simulations. We outline the strategy used to curate, adjust the monthly calendar aggregation and process the information downloaded from the ESGF. The results of these steps were used to perform analysis for several of the initial publications arising from PMIP4. We provide post-processed fields for each simulation, such as climatologies and common measures of variability. Example scripts used to visualise and analyse these fields are provided for several important case studies. [ABSTRACT FROM AUTHOR]

Over the recent decades, extreme precipitation events (EPEs) have become more frequent over the Sahel. Their properties, however, have so far received little attention. In this study the spatial distribution, intensity, seasonality, and interannual variability of EPEs are examined, using both a reference dataset based on a high-density rain gauge network over Burkina Faso and 24 precipitation gridded datasets. The gridded datasets are evaluated in depth over Burkina Faso while their commonalities are used to document the EPE properties over the Sahel. EPEs are defined as the occurrence of daily accumulated precipitation exceeding the all-day 99th percentile over a 1° × 1° pixel. Over Burkina Faso, this percentile ranges between 21 and 33 mm day−1. The reference dataset show that EPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. These results are consistent among the gridded datasets over Burkina Faso but also over the wider Sahel. The gridded datasets exhibit a wide diversity of skills when compared to the Burkinabe reference. The Global Precipitation Climatology Centre Full Data Daily version 1 (GPCC-FDDv1) and the Global Satellite Mapping of Precipitation Gauge Reanalysis version 6.0 (GSMaP-gauge-RNL v6.0) are the only products that properly reproduce all of the EPE features examined in this work. The datasets using a combination of microwave and infrared measurements are prone to overestimate the EPE intensity, while infrared-only products generally underestimate it. Their calibrated versions perform better than their uncalibrated (near-real-time) versions. This study finally emphasizes that the lack of rain gauge data availability over the whole Sahel strongly impedes our ability to gain insights in EPE properties. [ABSTRACT FROM AUTHOR]

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models. [ABSTRACT FROM AUTHOR]

In this study, a statistical analysis of the spatiotemporal characteristics of summer heavy hourly rainfall (HHR) events during 1979–2018 over mountainous Shanxi Province is performed in order to examine regional climate change over such a transition region between North China and Northwest China. Results show that the regional 40‐year annual mean summer rainfall is about 272 mm, about 37% of which is associated with HHR events. High‐frequency and high‐intensity HHR events tend to take place in eastern mountainous subregions, whereas most basin subregions experience HHR events with low frequency and low intensity, showing the important influences of local topography and its interaction with prevailing large‐scale flows. The important topographical influences are also reflected by the observations that most mountainous subregions exhibit a major late‐afternoon peak with a secondary early morning maximum, and that the associated diurnal variations are much greater in amplitude than those in basin subregions. It is found that the regional total annual summer rainfall has a decreasing trend during the first 20 years, mostly by non‐HHR contributions, but a rapid increasing trend during the recent 20 years, with dominant HHR contributions. Most subregions with the increasing trends of HHR frequency are those dominated by rapidly increasing surface temperatures. In particular, more frequent HHR events occur over major urbanization areas, that are in close proximity to complex topography, during the recent two decades. The decadal trends in HHR are found to be correlated to the distribution and intensity changes of the East Asian summer monsoon. The above results have important implications to the operational prediction of HHR events, and a better understanding of regional climate change over mountainous Shanxi Province. [ABSTRACT FROM AUTHOR]

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This study examines the development of a series of heavy rainfall events over four different geographical regions from central to north China on 19–21 July 2016, with the maximum 48‐hr‐accumulated and hourly rainfall amounts of 875 and 139 mm, respectively. Results show that the heavy rainfall events occurred in an environment with an anomalous deep trough associated with unusual extratropical cyclogenesis during this warm month and a pronounced moisture anomaly compared to a 30‐year climatology. The cyclogenesis coincided with dry‐air intrusion from the lower stratosphere, and its merging with a northwest and a southwest vortex in the lower troposphere, After its formation, the cyclone moved northward on the east side of Mt. Taihang and then northeastward across Mt. Yanshan. The presence of the southwest vortex and an abnormally strong western Pacific subtropical high contributed to the maintenance and intensification of a southwesterly low‐level jet (LLJ) carrying ample moisture, leading to the heavy rainfall in central China. The further enhanced southwesterly LLJ through the cyclogenesis and its accompanying southeasterly LLJ provided much needed precipitable water for the heavy rainfall events over north China. It was the quasi‐geostrophic forcing of the extratropical cyclone, together with its cold/warm frontal systems, and especially its northward movement with the approaching airflows near‐perpendicular to the general mountain orientations that provided optimized settings for the generation of a series of heavy rainfall events along the windward foothills of the major mountains in north China. [ABSTRACT FROM AUTHOR]