The IMPROVE-1 Storm of 1–2 February 2001. Part III: Sensitivity of a Mesoscale Model Simulation to the Representation of Snow Particle Types and Testing of a Bulk Microphysical Scheme with Snow Habit Prediction

Abstract A mesoscale model simulation of a wide cold-frontal rainband observed in the Pacific Northwest during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-1) field study was used to test the sensitivity of the model-produced precipitation to varied representations of snow particles in a bulk microphysical scheme. Tests of sensitivity to snow habit type, by using empirical relationships for mass and velocity versus diameter, demonstrated the defectiveness of the conventional assumption of snow particles as constant density spheres. More realistic empirical mass–diameter relationships result in increased numbers of particles and shift the snow size distribution toward larger particles, leading to increased depositional growth of snow and decreased cloud water production. Use of realistic empirical mass–diameter relationships generally increased precipitation at the surface as the rainband interacted with the orography, with more limited increases occurring offshore. Changes in both the mass–diameter and velocity–diameter relationships significantly redistributed precipitation either windward or leeward when the rainband interacted with the mountain barrier. A method of predicting snow particle habit in a bulk microphysical scheme, and using predicted habit to dynamically determine snow properties in the scheme, was developed and tested. The scheme performed well at predicting the habits present (or not present) in aircraft observations of the rainband. Use of the scheme resulted in little change in the precipitation rate at the ground for the rainband offshore, but significantly increased precipitation when the rainband interacted with the windward slope of the Olympic Mountains. The study demonstrates the promise of the habit prediction approach to treating snow in bulk microphysical schemes.

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Improvement of Microphysical Parameterization through Observational Verification Experiment

Bulletin of the American Meteorological Society ◽

10.1175/bams-84-12-1807 ◽

2003 ◽

Vol 84 (12) ◽

pp. 1807-1826 ◽

Cited By ~ 122

Author(s):

Mark T. Stoelinga ◽

Peter V. Hobbs ◽

Clifford F. Mass ◽

John D. Locatelli ◽

Brian A. Colle ◽

...

Keyword(s):

Pacific Northwest ◽

Mesoscale Model ◽

Model Resolution ◽

Precipitation Forecasting ◽

The Pacific ◽

Microphysical Parameters ◽

Forecast Models ◽

Intensive Observation ◽

Microphysical Processes ◽

Microphysical Parameterization

Despite continual increases in numerical model resolution and significant improvements in the forecasting of many meteorological parameters, progress in quantitative precipitation forecasting (QPF) has been slow. This is attributable in part to deficiencies in the bulk microphysical parameterization (BMP) schemes used in mesoscale models to simulate cloud and precipitation processes. These deficiencies have become more apparent as model resolution has increased. To address these problems requires comprehensive data that can be used to isolate errors in QPF due to BMP schemes from those due to other sources. These same data can then be used to evaluate and improve the microphysical processes and hydrometeor fields simulated by BMP schemes. In response to the need for such data, a group of researchers is collaborating on a study titled the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE). IMPROVE has included two field campaigns carried out in the Pacific Northwest: an offshore frontal precipitation study off the Washington coast in January–February 2001, and an orographic precipitation study in the Oregon Cascade Mountains in November–December 2001. Twenty-eight intensive observation periods yielded a uniquely comprehensive dataset that includes in situ airborne observations of cloud and precipitation microphysical parameters; remotely sensed reflectivity, dual-Doppler, and polarimetric quantities; upper-air wind, temperature, and humidity data; and a wide variety of surface-based meteorological, precipitation, and microphysical data. These data are being used to test mesoscale model simulations of the observed storm systems and, in particular, to evaluate and improve the BMP schemes used in such models. These studies should lead to improved QPF in operational forecast models.

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The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

The Cryosphere ◽

10.5194/tc-15-2781-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2781-2802

Author(s):

Linlu Mei ◽

Vladimir Rozanov ◽

Evelyn Jäkel ◽

Xiao Cheng ◽

Marco Vountas ◽

...

Keyword(s):

Grain Size ◽

Land Surface ◽

Model Simulation ◽

Atmospheric Correction ◽

Aircraft Measurements ◽

Airborne Measurements ◽

Snow Properties ◽

Snow Particle ◽

The Difference ◽

Mean Square Errors

Abstract. To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

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Impact of Moisture Flux and Freezing Level on Simulated Orographic Precipitation Errors over the Pacific Northwest

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-019.1 ◽

2013 ◽

Vol 14 (1) ◽

pp. 140-152 ◽

Cited By ~ 12

Author(s):

Yanluan Lin ◽

Brian A. Colle ◽

Sandra E. Yuter

Keyword(s):

Pacific Northwest ◽

Mesoscale Model ◽

Moisture Flux ◽

Orographic Precipitation ◽

Surface Precipitation ◽

Freezing Level ◽

The Pacific ◽

Using Data ◽

Oregon Cascades ◽

Ice Water

Abstract Two cool seasons (November–March) of daily simulations using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) over the Pacific Northwest are used to investigate orographic precipitation bias. Model simulations are compared with data from a radiosonde site at Salem, Oregon, just upstream (west) of the Oregon Cascades; precipitation gauges over a portion of the Pacific Northwest; and a National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) in Portland, Oregon. The 77 storms analyzed are partitioned into warm/cold storms based on the freezing level above/below the Oregon Cascades crest (~1600 m MSL). Although the seasonal precipitation is well simulated, the model has a tendency to overpredict surface precipitation for cold storms. The correlation between the upstream relative humidity–weighted integrated moisture transport and precipitation for warm storms (r2 = 0.81) is higher than that for cold storms (r2 = 0.54). Comparisons of model ice water content (IWC) and derived reflectivity with radar-retrieved IWC and observed reflectivity for the 38 well-simulated storms show reasonably good agreement for warm storms but an overprediction of IWC and reflectivity aloft for cold storms. One plausible reason for the persistent overprediction of IWC in cold storms might be related to the positive bias in snow depositional growth formulation in the model bulk microphysics parameterization. A favorable overlap of the maximum snow depositional growth region with the mountain wave ascent region in cold storms magnifies the bias and likely contributes to the precipitation overprediction. This study also highlights the benefit of using data aloft from an operational radar to complement surface precipitation gauges for model precipitation evaluation over mountainous terrain.

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The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

10.5194/tc-2020-270 ◽

2020 ◽

Author(s):

Linlu Mei ◽

Vladimir Rozanov ◽

Evelyn Jäkel ◽

Xiao Cheng ◽

Marco Vountas ◽

...

Keyword(s):

Grain Size ◽

Land Surface ◽

Model Simulation ◽

Atmospheric Correction ◽

Aircraft Measurements ◽

Airborne Measurements ◽

Snow Properties ◽

Snow Particle ◽

The Difference

Abstract. To evaluate the performance of eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in part 1 of the companion paper, this manuscript applies the XBAER algorithm on the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments onboard Sentinel-3. Snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS), and Specific Surface Area (SSA) are derived under cloud-free conditions. XBAER derived snow properties are compared to other existing satellite products and validated by ground-based/aircraft measurements. Cloud screening is performed by standard XBAER algorithm synergistically using OLCI and SLSTR instruments both onboard Sentinel-3. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) Aerosol Optical Thickness (AOT) and aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN simulated surface directional reflectances at 0.55 and 1.6 μm. The SSA is derived for a given SGS and SPS pair. XBAER derived SGS, SPS and SSA have been validated using in-situ measurements from the recent campaign SnowEx17 during February 2017. The comparison of the retrieved SGS with the in-situ data shows a relative difference between XBAER-derived SGS and SnowEx17 measured SGS of less than 4 %. The difference between XBAER-derived SSA and SnowEx17 measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonable-explained by the SnowEx17 observed snow particle shapes. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R = 0.82 and R = 0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability of the aircraft track of PAMARCMiP campaign. The comparison between XBAER-derived SGS results and MODIS Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonable-explained by campaign-based and laboratory investigations, indicating reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities of XBAER-derived SGS and SSA over both Greenland and Arctic-wide agree with the snow metamorphism process.

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