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Reliable precipitation forcing is essential for calculating the water balance, seasonal snowpack, glacier mass balance, streamflow, and other hydrological variables. However, satellite precipitation is often the only forcing available to run hydrological models in data-scarce regions, compromising hydrological calculations when unreliable. The IMERG product estimates precipitation quasi-globally from a combination of passive microwave and infrared satellites, which are intercalibrated based on GPM’s DPR and GMI instruments. Current GPM-DPR radar algorithms have satisfactorily estimated rainfall, but a limited consideration of PSD, attenuation correction, and ground clutter have degraded snowfall estimation, especially in mountain regions. This study aims to improve satellite radar snowfall estimates for this situation. Nearly two years (between 2019 and 2022) of aloft precipitation concentration, surface hydrometeor size, number and fall velocity, and surface precipitation rate from a high elevation site in the Canadian Rockies and collocated GPM-DPR reflectivities were used to develop a new snowfall estimation algorithm. Snowfall estimates using the new algorithm and measured GPM-DPR reflectivities were compared to other GPM-DPR-based products, including CORRA, which is employed to intercalibrate IMERG. Snowfall rates estimated with measured Ka reflectivities, and from CORRA were compared to MRR-2 observations, and had correlation, bias, and RMSE of 0.58 and 0.07, 0.43 and -0.38 mm h-1, and 0.83 and 0.85 mm h-1, respectively. Predictions using measured Ka reflectivity suggest that enhanced satellite radar snowfall estimates can be achieved using a simple measured reflectivity algorithm. These improved snowfall estimates can be adopted to intercalibrate IMERG in cold mountain regions, thereby improving regional precipitation estimates.
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Abstract. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020–2023. Precipitation phase observations showed a 2-m air temperature interval between 0–4 °C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5 °C, a linear transition model with dual temperature thresholds of –0.38 and 5 °C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance (F1 score of 0.74) due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. PGP_hydromet and PGP_full displayed the best classification performance (F1 score of 0.84). In terms of partitioning error, PGP_full had the lowest RMSE (0.27 mm) and the least variability in performance. The RMSE of the single temperature threshold model was the highest (0.40 mm) and showed the greatest performance variability. An input variable importance analysis revealed that the additional data used in the more complex PGP models mainly improved mixed-phase precipitation prediction. The improvement of mixed-phase prediction remains a challenge. Relative humidity was deemed the least important input variable used, due to consistent near water vapor saturation conditions. Additionally, the reanalysis atmospheric data proved to be an important factor to increase the robustness of the partitioning process. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.
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Abstract. Ice pellets can form when supercooled raindrops collide with small ice particles that can be generated through secondary ice production processes. The use of atmospheric models that neglect these collisions can lead to an overestimation of freezing rain. The objective of this study is therefore to understand the impacts of collisional freezing and secondary ice production on simulations of ice pellets and freezing rain. We studied the properties of precipitation simulated with the microphysical scheme Predicted Particle Properties (P3) for two distinct secondary ice production processes. Possible improvements to the representation of ice pellets and ice crystals in P3 were analyzed by simulating an ice pellet storm that occurred over eastern Canada in January 2020. Those simulations showed that adding secondary ice production processes increased the accumulation of ice pellets but led to unrealistic size distributions of precipitation particles. Realistic size distributions of ice pellets were obtained by modifying the collection of rain by small ice particles and the merging criteria of ice categories in P3.