A global evaluation of IMERG precipitation occurrence using SMAP detected soil moisture change

A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2x2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3x3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments.

HEAVY PRECIPITATION OCCURRENCE OVER KHERSON WEATHER STATION ACCORDING TO METEOROLOGICAL OBSERVATIONS AND ERA5 REANALYSIS

The paper presents the results of a study of heavy precipitation in the form of rain (> 30 mm/12 h) using data from the meteorological observations and atmospheric reanalysis ERA5 at the Kherson weather station in 2005-2021.Detected that at the Kherson there were only 19 cases of heavy rainfall, which occurred only in the warm half of the year with a maximum recurrence in July. Compared to 1961-1990, the number of heavy rains of 2005-2021 increased in July and June, and decreased in August.Determined that most of the real cases of increased precipitation in Kherson are in good agreement with the results of the ERA5 reanalysis, but in almost a third of the simulation episodes did not show heavy precipitation at the Kherson coordinates or their center was shifted.Heavy rains in Kherson were formed in a field of low atmospheric pressure, with a weak northwest wind and accompanied by thunderstorms.Clarified that most episodes of heavy rainfall in Kherson in 2005-2021 are associated with the movement of southern cyclones, others formed on the southern periphery of the anticyclone in the southwestern direction of the jet stream in the troposphere.

Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

High-Rate Precipitation Occurrence Modulated by Solar Wind High-Speed Streams

Extreme weather events, such as heavy rainfall causing floods and flash floods continue to present difficult challenges in forecasting. Using gridded daily precipitation datasets in conjunction with solar wind data it is shown that high-rate precipitation occurrence is modulated by solar wind high-speed streams. Superposed epoch analysis shows a statistical increase in the occurrence of high-rate precipitation following arrivals of high-speed streams from coronal holes, including their recurrence with the solar rotation period of 27 days. These results are consistent with the observed tendency of heavy rainfall leading to floods and flash floods in Japan, Australia, and continental United States to follow arrivals of high-speed streams. A possible role of the solar wind–magnetosphere–ionosphere–atmosphere coupling in weather as mediated by globally propagating aurorally excited atmospheric gravity waves triggering conditional moist instabilities leading to convection in the troposphere that has been proposed in previous publications is highlighted.

Investigating Various Products of IMERG for Precipitation Retrieval over Surfaces with and without Snow and Ice Cover

Precipitation rate from various products of the integrated multisatellite retrievals for GPM (IMERG) and passive microwave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the contiguous United States (CONUS) and using ground radar product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and combination of PMW and IR. It was found that precipitation estimates from PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces: (1) most PMW products show higher correlation coefficients than IR, (2) at cold temperatures (e.g., Tw < −10 °C), PMW products tend to underestimate and IR product shows large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.

Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling

Bias adjustment of climate model simulations is a common step in the climate impact assessment modeling chain. For precipitation intensity, multiple bias-adjusting methods have been developed, but less so for precipitation occurrence. Intensity-bias-adjusting methods such as ‘Quantile Delta Mapping’ can adjust too many wet days, but not too many dry days. Some occurrence-bias-adjusting methods have been developed to resolve this by the addition of the ability to adjust too dry simulations. Earlier research has shown this to be important when adjusting on a continental scale, when both types of biases can occur. However, the newer occurrence-bias-adjusting methods have their weakness: they might retain a bias in the number of dry days when adjusting data in a region that only has too many wet days. Yet, if this bias is small enough, it is more practical and economical to apply the newer methods when data in the larger region are adjusted. In this study, we consider two recently introduced occurrence-bias-adjusting methods, Singularity Stochastic Removal and Triangular Distribution Adjustment, and compare them in a region with only wet-day biases. This bias adjustment is performed for precipitation intensity and precipitation occurrence, while the evaluation is performed on precipitation intensity, precipitation occurrence and discharge, which combines the former two variables. Despite theoretical weaknesses, we show that both Singularity Stochastic Removal and Triangular Distribution Adjustment perform well. Thus, the methods can be applied for both too wet and too dry simulations, although Triangular Distribution Adjustment may be preferred as it was designed with a broad application in mind.

Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs)

In this study, we use analogue method and Convolutional Neural Networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on Large-Scale Meteorological Patterns (LSMPs) for the winter (DJF) of Pacific Coast California (PCCA) and the summer (JJA) of Midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures. Our results suggest that LSMPs provide useful predictability of daily extreme precipitation occurrence and its interannual variability over both regions. The 14-year (2006-2019) independent forecast shows Gilbert Skill Scores (GSS) in PCCA range from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across 4 analogue schemes, in contrast to those from 0.1 to 0.24 and from 0.1 to 0.14 in MWST. Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multi-variate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight the Integrated Vapor Transport (IVT, or its zonal and meridional components) enables higher skills than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.

Validation of CloudSat-CPR Derived Precipitation Occurrence and Phase Estimates across Canada

Snowfall affects the terrestrial climate system at high latitudes through its impacts on local meteorology, freshwater resources and energy balance. Precise snowfall monitoring is essential for cold countries such as Canada, and particularly in temperature-sensitive regions such as the Arctic; however, its size and remote location means the precipitation gauge network there is sparse. While satellite remote sensing of snowfall from instruments such as CloudSat-CPR offers a potential solution, satellite detection of precipitation phase has not been systematically evaluated across Canada. In this study, CloudSat-based precipitation occurrence and phase retrievals were validated at 26 stations across Canada maintained by Environment and Climate Change Canada (ECCC). Probability of Detection (POD), defined as the percentage agreement between coincident CloudSat and human-observed present weather information for precipitation (solid, liquid or no precipitation), and False Alarm Ratio (FAR) were used as the primary metrics for validation. The mean POD (FAR) for precipitation occurrence across Canada is 65.5% ± 4.3 (31.4% ± 5.1) and for no precipitation is 90.6% ± 1.4 (11% ± 2.5). The results show lower rates of detection under cloudier skies, in the presence of (freezing) drizzle and for lighter snowfall, which may be explained by a large number of false-positives due to CloudSat-CPR’s high instrumental sensitivity. When CloudSat correctly detects the occurrence of precipitation, it shows uniformly high POD (>80%) and low FAR (<10%) for classifying the phase of precipitation. Large databases of coincident ground and satellite measurements allow us to provide a new estimate of around 9% for the frequency of virga events, a factor of two smaller than a previous estimate for the Arctic. The results from this study show that CloudSat has useful accuracy in detecting precipitation occurrence and very high accuracy at classifying precipitation phase, over diverse climate zones across Canada. As such, there is significant potential for satellite monitoring of snowfall in remote, cold regions.

Effect of binary basicity on chromium occurrence in stainless steel slag

Comprehensive utilization of stainless-steel slag (SSS) is restrained due to the risk of Cr6+ leaching. Based on the studying the microstructure of synthetic slag (SS) containing Cr2O3with XRD, SEM-EDS?and Image pro, the effect of binary basicity on the chromium occurrence in SSS was investigated. The results indicated that the binary basicity had a significant impact on the properties of spinel crystals. There was a positive correlation between the calcium content in spinel crystals and the SS basicity. The size of spinel crystals varied from large to small and the precipitation occurrence changed with the basicity increase. Furthermore, the chromium occurrences changed with basicity. The chromium was produced in spinel crystals at lower basicity, but as the basicity increased to 3.0, the chromium precipitated as calcium chromate. In view of the relationship between the chromium leaching behavior and its occurrence, increasing basicity raised the Cr6+ leaching.

“It’s Raining Bits”: Patterns in Directional Precipitation Persistence across the United States

The spatial and temporal ordering of precipitation occurrence impacts ecosystems, streamflow, and water availability. For example, both large-scale climate patterns and local landscapes drive weather events, and the typical speeds and directions of these events moving across a basin dictate the timing of flows at its outlet. We address the predictability of precipitation occurrence at a given location, based on the knowledge of past precipitation at surrounding locations. We identify “dominant directions of precipitation influence” across the continental United States based on a gridded daily dataset. Specifically, we apply information theory–based measures that characterize dominant directions and strengths of spatial and temporal precipitation dependencies. On a national average, this dominant direction agrees with the prevalent direction of weather movement from west to east across the country, but regional differences reflect topographic divides, precipitation gradients, and different climatic drivers of precipitation. Trends in these information relationships and their correlations with climate indices over the past 70 years also show seasonal and spatial divides. This study expands upon a framework of information-based predictability to answer questions about spatial connectivity in addition to temporal persistence. The methods presented here are generally useful to understand many aspects of weather and climate variability.