Surface precipitation phase discrimination in complex terrain

2021 ◽  
Vol 592 ◽  
pp. 125780
Author(s):  
Enric Casellas ◽  
Joan Bech ◽  
Roger Veciana ◽  
Nicolau Pineda ◽  
Tomeu Rigo ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Surface Precipitation ◽  
Phase Discrimination ◽  
Precipitation Phase

scholarly journals Rain or Snow: Hydrologic Processes, Observations, Prediction, and Research Needs

2016 ◽  
Author(s):  
Adrian A. Harpold ◽  
Michael Kaplan ◽  
P. Zion Klos ◽  
Timothy Link ◽  
James P. McNamara ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Hydrological Modeling ◽  
Atmospheric Modeling ◽  
Hydrological Models ◽  
Near Surface ◽  
Surface Precipitation ◽  
Hydrologic Processes ◽  
Wide Range ◽  
Precipitation Phase ◽  
Observation Networks

Abstract. The phase of precipitation as snow or rain controls numerous hydrologic processes that are fundamental to effective hydrological modeling. Despite its foundational importance to terrestrial hydrology, typical phase prediction methods (PPM) use overly simplistic estimates based on near-surface air temperature. The review conveys the diversity of tools available for PPM in hydrological modeling and the advancements needed to improve predictions in complex terrain characterized by large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There are a wide range of options for field observations of precipitation phase, but a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require underlying assumptions and field validation before they are operational. We review the types and accuracy of common PPM to show accuracy is generally increased at finer time steps and by including humidity. One important tool for PPM development is atmospheric modeling, which offers numerous models and microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation phase observations. One important tool for PPM development is atmospheric modeling, which offers numerous models and microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation phase observations. The review concludes by describing key research gaps and recommendations to improve PPM. Recommendations include incorporate humidity information and atmospheric information into models, develop observation networks at high temporal resolutions, compare and validate different PPM, develop spatially resolved products, and characterize regional variability. PPM is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers with field hydrologists.

scholarly journals An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean

2016 ◽  
Vol 9 (4) ◽  
pp. 1637-1652 ◽  
Author(s):  
Jörg Burdanowitz ◽  
Christian Klepp ◽  
Stephan Bakan
Keyword(s):  
Mixed Phase ◽  
Global Ocean ◽  
Human Observer ◽  
Precipitation Data ◽  
Post Processing ◽  
Atmospheric Measurements ◽  
Data Set ◽  
Surface Precipitation ◽  
Precipitation Phase

Abstract. The lack of high-quality in situ surface precipitation data over the global ocean so far limits the capability to validate satellite precipitation retrievals. The first systematic ship-based surface precipitation data set OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) aims at providing a comprehensive statistical basis of in situ precipitation reference data from optical disdrometers at 1 min resolution deployed on various research vessels (RVs). Deriving the precipitation rate for rain and snow requires a priori knowledge of the precipitation phase (PP). Therefore, we present an automatic PP distinction algorithm using available data based on more than 4 years of atmospheric measurements onboard RV Polarstern that covers all climatic regions of the Atlantic Ocean. A time-consuming manual PP distinction within the OceanRAIN post-processing serves as reference, mainly based on 3-hourly present weather information from a human observer. For automation, we find that the combination of air temperature, relative humidity, and 99th percentile of the particle diameter predicts best the PP with respect to the manually determined PP. Excluding mixed phase, this variable combination reaches an accuracy of 91 % when compared to the manually determined PP for 149 635 min of precipitation from RV Polarstern. Including mixed phase (165 632 min), an accuracy of 81.2 % is reached for two independent PP distributions with a slight snow overprediction bias of 0.93. Using two independent PP distributions represents a new method that outperforms the conventional method of using only one PP distribution to statistically derive the PP. The new statistical automatic PP distinction method considerably speeds up the data post-processing within OceanRAIN while introducing an objective PP probability for each PP at 1 min resolution.

scholarly journals Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals

2020 ◽  
Vol 51 (2) ◽  
pp. 180-187
Author(s):  
James M. Feiccabrino
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Phase Error ◽  
Dew Point ◽  
Time Step ◽  
Meteorological Forcing ◽  
Surface Precipitation ◽  
Phase Uncertainty ◽  
Meteorological Observations ◽  
Precipitation Phase

Abstract Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35–65% from the use of 1–24 h data. Within a sub-dataset of observations occurring between AT −6 and 6 °C representing 57% of annual precipitation, misclassified precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.

scholarly journals Rain or snow: hydrologic processes, observations, prediction, and research needs

2017 ◽  
Vol 21 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Adrian A. Harpold ◽  
Michael L. Kaplan ◽  
P. Zion Klos ◽  
Timothy Link ◽  
James P. McNamara ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Hydrological Modeling ◽  
Regional Scale ◽  
Atmospheric Modeling ◽  
Hydrological Models ◽  
Scientific Cooperation ◽  
Near Surface ◽  
Hydrologic Processes ◽  
Wide Range ◽  
Precipitation Phase

Abstract. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.

Statistical and Physical Analysis of the Vertical Structure of Precipitation in the Mountainous West Region of the United States Using 11+ Years of Spaceborne Observations from TRMM Precipitation Radar

2013 ◽  
Vol 52 (2) ◽  
pp. 408-424 ◽  
Author(s):  
Qing Cao ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
Youcun Qi ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Vertical Structure ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Physical Analysis ◽  
Precipitation Radar ◽  
Surface Precipitation ◽  
Freezing Level ◽  
Physically Based ◽  
Trmm Precipitation

AbstractThis study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the characteristics of VPRs, the shape of VPRs also influences the intensity of surface rainfall rates, the characteristics of VPRs have a seasonal dependence with strong similarities between the spring and autumn months, and the spatial variation of VPR characteristics suggests that the underlying terrain has an impact on the vertical structure. The comprehensive statistical and physical analysis strengthens the understanding of the vertical structure of precipitation and advocates for the approach of VPR correction to improve surface precipitation estimation in complex terrain.

scholarly journals An automatic precipitation phase distinction algorithm for optical disdrometer data over the global ocean

2015 ◽  
Vol 8 (12) ◽  
pp. 13645-13691
Author(s):  
J. Burdanowitz ◽  
C. Klepp ◽  
S. Bakan
Keyword(s):  
A Priori ◽  
Particle Diameter ◽  
Mixed Phase ◽  
Global Ocean ◽  
Human Observer ◽  
Post Processing ◽  
Atmospheric Measurements ◽  
Surface Precipitation ◽  
Precipitation Phase

Abstract. The lack of high quality in situ surface precipitation data over the global ocean so far limits the capability to validate satellite precipitation retrievals. The first systematic ship-based surface precipitation dataset OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) aims at providing a comprehensive statistical basis of in situ precipitation reference data from optical disdrometers at 1 min resolution deployed on various research vessels (RVs). Deriving the precipitation rate for rain and snow requires a priori knowledge of the precipitation phase (PP). Therefore, we present an automatic PP distinction algorithm using available data based on more than four years of atmospheric measurements onboard RV Polarstern that covers all climatic regions of the Atlantic Ocean. A time-consuming manual PP distinction within the OceanRAIN post-processing serves as reference, mainly based on 3 hourly present weather information from a human observer. For automation, we find that the combination of air temperature, relative humidity and 99th percentile of the particle diameter predicts best the PP with respect to the manually determined PP. Excluding mixed-phase, this variable combination reaches an accuracy of 91 % when compared to the manually determined PP for about 149 000 min of precipitation from RV Polarstern. Including mixed-phase (165 000 min), 81.2 % accuracy are reached with a slight snow overprediction bias of 0.93 for two independent PP distributions. In that respect, a method using two independent PP distributions outperforms a method based on only one PP distribution. The new statistical automatic PP distinction method significantly speeds up the data post-processing within OceanRAIN while introducing an objective PP probability for each PP at 1 min resolution.

A Case Study of Processes Impacting Precipitation Phase and Intensity during the Vancouver 2010 Winter Olympics

2012 ◽  
Vol 27 (6) ◽  
pp. 1301-1325 ◽  
Author(s):  
Julie M. Thériault ◽  
Roy Rasmussen ◽  
Trevor Smith ◽  
Ruping Mo ◽  
Jason A. Milbrandt ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Weather Conditions ◽  
Winter Storms ◽  
Winter Olympics ◽  
Precipitation Forecasting ◽  
Isothermal Layer ◽  
Scale Models ◽  
Model Predictions ◽  
Precipitation Phase

Abstract Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain.

scholarly journals Examination of Mixed-Phase Precipitation Forecasts from the High-Resolution Rapid Refresh Model Using Surface Observations and Sounding Data

2017 ◽  
Vol 32 (3) ◽  
pp. 949-967 ◽  
Author(s):  
Kyoko Ikeda ◽  
Matthias Steiner ◽  
Gregory Thompson
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Temperature Profile ◽  
Weather Prediction ◽  
Mixed Phase ◽  
Phase Precipitation ◽  
Vertical Temperature ◽  
Surface Precipitation ◽  
Temperature Bias ◽  
Precipitation Phase

Abstract Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer.

scholarly journals The Precipitation Imaging Package: Phase Partitioning Capabilities

2021 ◽  
Vol 13 (11) ◽  
pp. 2183
Author(s):  
Claire Pettersen ◽  
Larry F. Bliven ◽  
Mark S. Kulie ◽  
Norman B. Wood ◽  
Julia A. Shates ◽  
...  
Keyword(s):  
Phase Transition ◽  
Phase Identification ◽  
Near Surface ◽  
Surface Precipitation ◽  
Phase Partitioning ◽  
Thermodynamic Conditions ◽  
Lapse Rates ◽  
Precipitation Phase

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.

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