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This study conducted evaluation and analysis on various precipitation products over the eastern Tibetan Plateau (ETP), including four sets of satellite precipitation data (i.e., IMERG_Uncal, IMERG_Cal, GSMaP_MVK, GSMaP_Gauge) and one set of model reanalysis data (i.e., ERA5‐land, hereafter ERA5‐L). We evaluated the spatial‐temporal distribution of their quality at an hourly temporal scale and 0.1° spatial scale, with a special focus on capturing different types of precipitation. The results show that: (a) GSMaP_Gauge exhibits the highest correlation with ground‐based gauges, while IMERG_Uncal and IMERG_Cal perform best in the estimation of the amount of precipitation. Satellite products generally perform better during summer while ERA5‐L sometimes outperforms satellite products in spring and autumn. (b) The evaluation results for different precipitation types reveal that all the QPE products face significant challenges in accurately describing convective precipitation. They tend to underestimate convective precipitation and fail to properly capture the intensity and location of heavy precipitation. (c) In heavy convective precipitation cases, the evaluated QPE products show various issues in accurately capturing the intensity and spatiotemporal variation of precipitation. Almost all QPE products underestimate maximum precipitation (both hourly precipitation and accumulated precipitation) and small‐scale (about 50 km or less) spatial variability of precipitation. IMERG_Uncal, IMERG_Cal, and GSMaP_MVK perform better than other products in heavy convective precipitation cases. This study provides new insights into the quality of QPE products in different types of precipitation. The analysis of the quality of these QPE products serves as a valuable indicator of their potential applications, particularly in flash flood simulations, while also underscoring the critical need for improving precipitation product quality. Plain Language Summary The eastern Tibetan Plateau (ETP) has significant research value in the fields of meteorology and hydrology. Therefore, accurate precipitation information is essential in this region. Quantitative precipitation estimation (QPE) traditionally relies on ground‐based rain gauges, but this method has limitations due to the scarcity and uneven distribution of gauges in mountainous regions. Satellite and reanalysis gridded QPE products provide precipitation information for areas without rain gauge observations. However, it is essential to verify the accuracy of the precipitation information provided by comparing it with ground observation data from rain gauges. In meteorology, precipitation is categorized as stratiform precipitation, convective precipitation. Different types of precipitation have varying causes and characteristics, and QPE products may capture and characterize them with varying degrees of difficulty. So far, quantitative studies are still lacking on how competently QPE products describe different types of precipitation. This study includes four sets of satellite precipitation data and one set of reanalysis data. We compared their correlation and bias with rain gauges in different types of precipitation. After understanding that these products performed the worst in convective precipitation, we analyzed their problem of describing intensity, spatiotemporal structure, and location precipitation in strong convective precipitation. Key Points Satellite QPE products show better quality than ERA5‐L in summer Of all precipitation types, convective precipitation is the most challenging for QPE products due to underestimation The QPE products have difficulty showing the small‐scale spatial‐temporal structure of precipitation

Weather forecasts and climate projections of precipitation phase and type in winter storms are challenging due to the complicated underlying microphysical and dynamical processes. In the Canadian numerical weather prediction model, explicit freezing rain (FR) at the surface is often overestimated during the winter season for situations in which snow is observed. For a case study simulated using this model with the Predicted Particle Properties (P3) microphysics scheme, the secondary ice production (SIP) process has a major impact on the surface precipitation type. Parameterized SIP substantially reduces FR due to increased collection of supercooled drops with ice particles formed by rime splintering. Hindcast simulations of 40 winter cases show that these results are systematic, and the decreased frequency of FR leads to improved forecast skill relative to observations. Thus, accounting for SIP in the model is critical for accurately simulating precipitation types. Plain Language Summary Several types of winter precipitation, including snow, freezing rain (FR) and ice pellets (IP), are associated with hazards such as injuries from people falling, disruption of electrical supply, and breakdown of transportation networks due to the accumulation of ice on surfaces. Forecasts of precipitation type using weather prediction models, as well as projections for a warmer climate, are challenging because of the complicated physical processes involved. In this article, it is shown that the component of the numerical model that is used to represent clouds and precipitation in the Canadian high‐resolution weather forecast system overestimates FR at the expense of snow. This problem is mitigated when the additional process of secondary ice production (here, the generation of new ice particles from collisions of existing ice and supercooled drops) is included in the model. The presence of numerous small ice particles formed by this process decreases the amount of FR and improves forecast skill scores for 40 historic winter cases. Thus, accounting for this process in weather and climate models is important for accurately simulating FR, IP and snow. Key Points Secondary ice production (SIP) substantially impacts precipitation phase and type in winter storms Inclusion of SIP reduces excessive freezing rain (FR) in numerical simulations Forecast metrics for FR in 40 winter cases are improved when SIP is included in a weather model

Ammonia (NH 3 ) is a key precursor of secondary inorganic aerosols. During precipitation, NH 3 in the atmosphere can be captured by rain and converted to NH 4 + , whereas during evaporation, NH 4 + can become NH 3 and be released again. The northeastern region of China experiences diverse precipitation types, making the study of the NH 3 release flux and its influencing factors during evaporation highly significant. In this study, precipitation samples of haze (HZ), dust (DS), convective (CC), and monsoon (MN) events were collected three times in Changchun from March to September 2024 (a total of twelve rain events), and indoor simulation evaporation experiments were conducted. The results revealed significant differences in the NH 4 + conversion rate ( R ), NH 3 release flux ( F ) and release rate ( V ) across the precipitation types ( P  < 0.05). The NH 3 flux released from precipitation evaporation was 20.33 µg/m 2 in spring and 64.53 µg/m 2 in summer, accounting for approximately 4.14% and 7.70%, respectively, of the corresponding atmospheric NH 3 concentrations. Meteorological factors influenced NH 3 release similarly across precipitation types. R peaked and then decreased with increasing temperature and was significantly negatively correlated with wind speed and precipitation amount ( P  < 0.05). In addition, this study calculates the temperature coefficient ( K 1 ), wind speed coefficient ( K 2 ), and precipitation amount coefficient ( K 3 ) by considering these factors. These findings provide valuable insights for estimating NH 3 release fluxes from precipitation evaporation in different regions.

Winter weather events with temperatures near 0 ∘ C are often associated with freezing rain. They can have major impacts on the society by causing power outages and disruptions to the transportation networks. Despite the catastrophic consequences of freezing rain, very few studies have investigated how their occurrences could evolve under climate change. This study aims to investigate the change of freezing rain and ice pellets over southern Québec using regional climate modeling at high resolution. The fifth-generation Canadian Regional Climate Model with climate scenario RCP 8.5 at 0 . 11 ∘ grid mesh was used. The precipitation types such as freezing rain, ice pellets or their combination are diagnosed using five methods (Cantin and Bachand, Bourgouin, Ramer, Czys and, Baldwin). The occurrences of the diagnosed precipitation types for the recent past (1980–2009) are found to be comparable to observations. The projections for the future scenario (2070–2099) suggested a general decrease in the occurrences of mixed precipitation over southern Québec from October to April. This is mainly due to a decrease in long-duration events ( ≥ 6 h ). Overall, this study contributes to better understand how the distribution of freezing rain and ice pellets might change in the future using high-resolution regional climate model.

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation, but also on its type. Uncertainty related to determination of the precipitation type (PT) leads to financial losses in many areas of human activity, such as the power industry, agriculture, transportation, and many more. In this study, we use machine learning (ML) algorithms with the data fusion approach to more accurately determine surface PT. Based on surface synoptic observations, ERA5 reanalysis, and radar data, we distinguish between liquid, mixed, and solid precipitation types. The study domain considers the entire area of Poland and a period from 2015 to 2017. The purpose of this work is to address the question: “How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?” Despite testing 33 parameters, it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0–1000 m above ground level (AGL) layer contains most of the signal needed to determine surface PT. The accrued probability of detection for liquid, solid, and mixed PTs according to the developed Random Forest model is 98.0%, 98.8%, and 67.3%, respectively. The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

Accurate precipitation data play important roles in climate and hydrology research at regional and global scales. The correction of system errors associated with precipitation represents a feasible and effective way to improve the accuracy of rainfall data. We analysed and corrected rainfall data collected at 45 meteorological sites in Xinjiang over 55 years (from 1 January 1960 to 31 December 2014) in terms of the wetting loss, trace precipitation and wind-induced loss, with 2 judgement methods of precipitation types. We based our analysis on daily temperature, wind speed, precipitation, relative humidity and air pressure data. Precipitation after correction was compared to precipitation before correction. The primary conclusions are: (1) Based on the new method of distinguishing precipitation types, the results show more snow days, fewer mixed precipitation days and slightly fewer rain days than the traditional method. (2) The sum values of corrections for each loss based on the new method of distinguishing precipitation types are higher than those based on the traditional method in spring and autumn. The sum values of corrections and differences of each loss are all larger in North Xinjiang and smaller in South Xinjiang. The sum values of total corrections are larger on the north slope of the Tianshan Mountains and smaller on the south slope, and they decrease from the south slope to South Xinjiang. (3) The median values of the sum of each loss and total correction in the northern region of Xinjiang are higher than those in the central region of Xinjiang, which are higher than those in the southern region of Xinjiang on the whole. (4) Precipitation increases after correction. The annual mean corrected values at Bayanbulak (in the Tianshan Mountains), Altay (in North Xinjiang) and Yutian (in South Xinjiang) are 81.48, 54.60 and 12.55 mm, respectively.

To address the need to map precipitation on a global scale, a collection of satellites carrying passive microwave (PMW) radiometers has grown over the last 20 years to form a constellation of about 10–12 sensors at any one time. Over the same period, a broad range of science and user communities has become increasingly dependent on the precipitation products provided by these sensors. The constellation presently consists of both conical and cross-track-scanning precipitation-capable multichannel instruments, many of which are beyond their operational and design lifetime but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations and the Coordinating Group for Meteorological Satellites (CGMS), among other groups, have raised the issue of how a robust, future precipitation constellation should be constructed. The key issues of current and future requirements for the mapping of global precipitation from satellite sensors can be summarized as providing 1) sufficiently fine spatial resolutions to capture precipitation-scale systems and reduce the beam-filling effects of the observations; 2) a wide channel diversity for each sensor to cover the range of precipitation types, characteristics, and intensities observed across the globe; 3) an observation interval that provides temporal sampling commensurate with the variability of precipitation; and 4) precipitation radars and radiometers in low-inclination orbit to provide a consistent calibration source, as demonstrated by the first two spaceborne radar–radiometer combinations on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission Core Observatory. These issues are critical in determining the direction of future constellation requirements while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

Typhoon rainstorms often cause disasters in southern China. Quantitative precipitation estimation (QPE) with the use of polarimetric radar can improve the accuracy of precipitation estimation and enhance typhoon defense ability. On the basis of the observed drop size distribution (DSD) of raindrops, a comparison is conducted among the DSD parameters and the polarimetric radar observation retrieved from DSD in five typhoon and three squall line events that occurred in southern China from 2016 to 2017. A new piecewise fitting method (PFM) is used to develop the QPE estimators for landfall typhoons and squall lines. The performance of QPE is evaluated by two fitting methods for two precipitation types using DSD data collected. Findings indicate that the number concentration of raindrops in typhoon precipitation is large and the average diameter is small, while the raindrops in squall line rain have opposite characteristics. The differential reflectivity (ZDR) and specific differential phase (KDP) in these two precipitation types increase slowly with the reflectivity factor (ZH), whereas the two precipitation types have different ZDR and KDP in the same ZH. Thus, it is critical to fit the rainfall estimator for different precipitation types. Enhanced estimation can be obtained using the estimators for specific precipitation types, whether the estimators are derived from the conventional fitting method (CFM) or PFM, and the estimators fitted using the PFM can produce better results. The estimators for the developed polarimetric radar can be used in operational QPE and quantitative precipitation foresting, and they can improve disaster defense against typhoons and heavy rains.

An 8‐year climatology of raintype observations from the Global Precipitation Measurement dual‐frequency precipitation radar highlights two stratiform rain producing storm regimes differentiated by the relative importance of convection. The more convectively active regime occurs in the tropics and over warm‐season midlatitude land, where warm‐ and cold‐topped convection accounts for 55% (40%) of the rain (rain area). The less convectively active regime dominates over midlatitude ocean and cold‐season midlatitude land, where convective rain only accounts for 15% (8%) of the rain (rain area). The ratio between cold‐topped convective and stratiform rain area is highly distinct between the two regimes (22% vs. 5.5%), with different precipitation amounts (and thus heating) for similar stratiform rain areas. A third tropics‐only warm‐topped convection regime exists, but is not associated with major stratiform rain production. Plain Language Summary We separate global precipitation into convective and stratiform types using a spaceborne weather radar, where convective rain is intense and short‐lived and stratiform rain is weaker and more homogeneous in space and time. We show that there are two unique regimes between these precipitation types that are dictated by how important convective processes are to a storm's evolution, that is, cold‐topped convection is either essential to sustaining the storm system or more of a bystander to large‐scale atmospheric processes that prefer to produce stratiform precipitation. The more convective regime is dominant in the tropics but can also occur over land in the midlatitudes during summer. This regime requires strong interaction between the convective and stratiform rainy regions and is more likely to produce large rain amounts. The less convective regime is predominant over higher latitude oceans and over land in the midlatitudes during winter. While convective and stratiform rain occurrence is well correlated in this regime, convective rain does not contribute significantly to the overall precipitation amounts. We believe that these regimes support a reevaluation of how precipitation physics and their interactions are handled in climate models. Key Points Areal coverage and echo‐top heights of stratiform rain and warm‐ and cold‐topped convection indicate strong geographic variability Stratiform rain fraction is 45% and 85% in the tropics and midlatitudes, respectively, with a rapid transition in the subtropics Statistical analysis suggests unique interactions between cold‐top convection and stratiform precipitation in the tropics and extratropics

Long-term changes in convective and stratiform precipitation in Northern Eurasia (NE) over the last five decades are estimated. Different types of precipitation are separated according to their genesis using routine meteorological observations of precipitation, weather conditions, and morphological cloud types for the period 1966-2016. From an initial 538 stations, the main analysis is performed for 326 stations that have no gaps and meet criteria regarding the artificial discontinuity absence in the data. A moderate increase in total precipitation over the analyzed period is accompanied by a relatively strong growth of convective precipitation and a concurrent decrease in stratiform precipitation. Convective and stratiform precipitation totals, precipitation intensity and heavy precipitation sums depict major changes in summer, while the relative contribution of the two precipitation types to the total precipitation (including the contribution of heavy rain events) show the strongest trends in transition seasons. The contribution of heavy convective showers to the total precipitation increases with the statistically significant trend of 1%-2% per decade in vast NE regions, reaching 5% per decade at a number of stations. The largest increase is found over the southern Far East region, mostly because of positive changes in convective precipitation intensity with a linear trend of more than 1 mm/day/decade, implying a 13.8% increase per 1 °C warming. In general, stratiform precipitation decreases over the majority of NE regions in all seasons except for winter. This decrease happens at slower rates in comparison to the convective precipitation changes. The overall changes in the character of precipitation over the majority of NE regions are characterized by a redistribution of precipitation types toward more heavy showers.