scholarly journals The 13–14 December 2001 IMPROVE-2 Event. Part III: Simulated Microphysical Budgets and Sensitivity Studies

2005 ◽  
Vol 62 (10) ◽  
pp. 3535-3558 ◽  
Author(s):  
Brian A. Colle ◽  
Matthew F. Garvert ◽  
Justin B. Wolfe ◽  
Clifford F. Mass ◽  
Christopher P. Woods
Keyword(s):  
Water Vapor ◽  
Mesoscale Model ◽  
Positive Impact ◽  
Cloud Water ◽  
Precipitation Forecast ◽  
State University ◽  
Microphysical Process ◽  
Surface Precipitation ◽  
Sensitivity Studies ◽  
Microphysical Parameterization

Abstract This paper investigates the microphysical pathways and sensitivities within the Reisner-2 bulk microphysical parameterization (BMP) of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) for the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE)-2 field experiment on 13–14 December 2001. A microphysical budget over the windward slope at 1.33-km horizontal grid spacing was calculated, in which the importance of each microphysical process was quantified relative to the water vapor loss (WVL) rate. Over the windward Cascades, the largest water vapor loss was associated with condensation (73% of WVL) and snow deposition (24%), and the windward surface precipitation resulted primarily from accretion of cloud water by rain (27% of WVL), graupel fallout and melt (19%), and snowmelt (6%). Two-thirds of the snow generated aloft spilled over into the lee in an area of model overprediction, resulting in windward precipitation efficiency of only 50%. Even with the large amount of precipitation spillover, the windward precipitation was still overpredicted in many locations. A series of experiments were completed using different snowfall speeds, cloud water autoconversion, threshold riming values for snow to graupel autoconversion, and slope intercepts for snow. The surface precipitation was most sensitive to those parameters associated with the snow size distribution and fall speed, while decreasing the riming threshold for snow to graupel conversion had the greatest positive impact on the precipitation forecast. All simulations overpredicted cloud water over the lower windward slopes, had too little cloud water over the crest, and had too much ice at moderate-to-large sizes aloft. Riming processes were important, since without supercooled water there were bull’s-eyes of spurious snow spillover over the lee slopes.

Changes in Water Vapor Transport and the Production of Precipitation in the Eastern Fertile Crescent as a Result of Global Warming

2008 ◽  
Vol 9 (6) ◽  
pp. 1390-1401 ◽  
Author(s):  
J. P. Evans
Keyword(s):  
Global Warming ◽  
Water Vapor ◽  
Climate Model ◽  
Mesoscale Model ◽  
Regional Climate ◽  
Water Vapor Transport ◽  
State University ◽  
Storm Events ◽  
Fertile Crescent ◽  
Emissions Scenarios

Abstract This study investigates changes in the types of storm events occurring in the Fertile Crescent as a result of global warming. Regional climate model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah] simulations are run for the first and last five years of the twenty-first century following the Special Report on Emissions Scenarios (SRES) A2 experiment. Then the precipitation events are classified according to the water vapor fluxes that created them. At present most of the region’s precipitation is from westerly water vapor fluxes. Results indicate that the region will increasingly get its precipitation from large events that are dominated by southerly water vapor fluxes. The increase in these events will occur in the transition seasons, especially autumn.

scholarly journals The 13–14 December 2001 IMPROVE-2 Event. Part II: Comparisons of MM5 Model Simulations of Clouds and Precipitation with Observations

2005 ◽  
Vol 62 (10) ◽  
pp. 3520-3534 ◽  
Author(s):  
Matthew F. Garvert ◽  
Christopher P. Woods ◽  
Brian A. Colle ◽  
Clifford F. Mass ◽  
Peter V. Hobbs ◽  
...  
Keyword(s):  
Liquid Water ◽  
Mesoscale Model ◽  
Heavy Precipitation ◽  
Precipitation Event ◽  
State University ◽  
Cloud Liquid Water ◽  
Aircraft Flight ◽  
Oregon Cascades ◽  
Microphysical Parameterization ◽  
Mass Concentrations

Abstract This paper compares airborne in situ observations of cloud microphysical parameters with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) simulations, using the Reisner-2 bulk microphysical parameterization, for a heavy precipitation event over the Oregon Cascades on 13–14 December 2001. The MM5 correctly replicated the extent of the snow field and the growth of snow particles by vapor deposition measured along aircraft flight tracks between altitudes of 4.9 and 6 km, but overpredicted the mass concentrations of snow. The model produced a broader number distribution of snow particles than observed, overpredicting the number of moderate-to-large-sized snow particles and underpredicting the number of small particles observed along the aircraft flight track. Over the mountain crest, the model overpredicted depositional growth of snow and mass concentrations of snow, but underpredicted the amount of cloud liquid water and conversion of snow to graupel. The misclassification of graupel as snow and excessive amounts of snow resulted in the model overpredicting precipitation on the lee slopes and in localized areas along the foothills of the Cascades. The model overpredicted cloud liquid water over the lower windward slopes and foothills, where accretion of cloud liquid water by rain was the primary precipitation-producing mechanism.

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

Isotopic Fractionation in Wintertime Orographic Clouds

2016 ◽  
Vol 33 (12) ◽  
pp. 2663-2678 ◽  
Author(s):  
Douglas Lowenthal ◽  
A. Gannet Hallar ◽  
Ian McCubbin ◽  
Robert David ◽  
Randolph Borys ◽  
...  
Keyword(s):  
Water Vapor ◽  
Isotopic Composition ◽  
Mesoscale Model ◽  
Isotopic Fractionation ◽  
Early Morning ◽  
Cloud Water ◽  
Mixed Phase ◽  
Vapor Source ◽  
Vapor Cloud ◽  
Orographic Clouds

AbstractThe Isotopic Fractionation in Snow (IFRACS) study was conducted at Storm Peak Laboratory (SPL) in northwestern Colorado during the winter of 2014 to elucidate snow growth processes in mixed-phase clouds. The isotopic composition (δ18O and δD) of water vapor, cloud water, and snow in mixed-phase orographic clouds were measured simultaneously for the first time. The depletion of heavy isotopes [18O and deuterium (D)] was greatest for vapor, followed by snow, then cloud. The vapor, cloud, and snow compositions were highly correlated, suggesting similar cloud processes throughout the experiment. The isotopic composition of the water vapor was directly related to its concentration. Isotopic fractionation during condensation of vapor to cloud drops was accurately reproduced assuming equilibrium fractionation. This was not the case for snow, which grows by riming and vapor deposition. This implies stratification of vapor with altitude. The relationship between temperature at SPL and δ18O was used to show that the snow gained most of its mass within 922 m above SPL. Relatively invariant deuterium excess (d) in vapor, cloud water, and snow from day to day suggests a constant vapor source and Rayleigh fractionation during transport. The diurnal variation of vapor d reflected the differences between surface and free-tropospheric air during the afternoon and early morning hours, respectively. These observations will be used to validate simulations of snow growth using an isotope-enabled mesoscale model with explicit microphysics.

scholarly journals High-Resolution Simulations and Microphysical Validation of an Orographic Precipitation Event over the Wasatch Mountains during IPEX IOP3

2005 ◽  
Vol 133 (10) ◽  
pp. 2947-2971 ◽  
Author(s):  
Brian A. Colle ◽  
Justin B. Wolfe ◽  
W. James Steenburgh ◽  
David E. Kingsmill ◽  
Justin A. W. Cox ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Salt Lake ◽  
Great Salt Lake ◽  
Cloud Water ◽  
Precipitation Event ◽  
Convergence Zone ◽  
Grid Spacing ◽  
Surface Precipitation ◽  
Wasatch Mountains ◽  
Precipitation Enhancement

Abstract This paper investigates the kinematic flow and precipitation evolution of a winter storm over and upstream of the Wasatch Mountains [Intermountain Precipitation Experiment third intensive observing period (IPEX IOP3)] using a multiply nested version of the fifth-generation Pennsylvania State University (PSU)––National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5). Validation using in situ aircraft data, radiosondes, ground-based radar, and surface observations showed that the MM5, which featured four domains with 36-, 12-, 4-, and 1.33-km grid spacing, realistically simulated the observed partial blocking of the 8–12 m s−1 ambient southwesterly flow and development of a convergence zone and enhanced lowland precipitation region upwind of the initial Wasatch slope. The MM5 also properly simulated the advance of this convergence zone toward the base of the Wasatch during the passage of a midlevel trough, despite not fully capturing the westerly wind shift accompanying the trough. Accurate simulation of the observed precipitation over the central Wasatch Mountains (within 25% of observed at all stations) required a horizontal grid spacing of 1.33 km. Despite close agreement with the observed surface precipitation, the Reisner2 bulk microphysical scheme produced too much supercooled cloud water and too little snow aloft. A model microphysical budget revealed that the Reisner2 generated over half of the surface precipitation through riming and accretion, rather than snow deposition and aggregation as implied by the observations. Using an intercept for the snow size distribution that allows for greater snow concentrations aloft improved the snow predictions and reduced the cloud water overprediction. Sensitivity studies illustrate that the reduced surface drag of the Great Salt Lake (GSL) enhanced the convergence zone and associated lowland precipitation enhancement upstream of the Wasatch Mountains. The presence of mountain ranges south of the Great Salt Lake appears to have weakened the along-barrier flow and windward convergence, resulting in a slight decrease in windward precipitation enhancement. Diabatic cooling from falling precipitation was also important for maintaining the blocked flow.

scholarly journals Four-Dimensional Variational Data Analysis of Water Vapor Raman Lidar Data and Their Impact on Mesoscale Forecasts

2008 ◽  
Vol 25 (8) ◽  
pp. 1437-1453 ◽  
Author(s):  
Matthias Grzeschik ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Dirk Engelbart ◽  
Ulla Wandinger ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Analysis ◽  
Vertical Structure ◽  
Mesoscale Model ◽  
Test Case ◽  
State University ◽  
Raman Lidar ◽  
Initial Water ◽  
Forecast Time ◽  
The Impact

Abstract The impact of water vapor observations on mesoscale initial fields provided by a triangle of Raman lidar systems covering an area of about 200 km × 200 km is investigated. A test case during the Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH-2005) was chosen. Evaluation of initial water vapor fields derived from ECMWF analysis revealed that in the model the highly variable vertical structure of water vapor profiles was not recovered and vertical gradients were smoothed out. Using a 3-h data assimilation window and a resolution of 10–30 min, continuous water vapor data from these observations were assimilated in the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) by means of a four-dimensional variational data analysis (4DVAR). A strong correction of the vertical structure and the absolute values of the initial water vapor field of the order of 1 g kg−1 was found. This occurred mainly upstream of the lidar systems within an area, which was comparable with the domain covered by the lidar systems. The correction of the water vapor field was validated using independent global positioning system (GPS) sensors. Much better agreement to GPS zenith wet delay was achieved with the initial water vapor field after 4DVAR. The impact region was transported with the mean wind and was still visible after 4 h of free forecast time.

Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events

2013 ◽  
Vol 52 (4) ◽  
pp. 889-902 ◽  
Author(s):  
Hongli Wang ◽  
Juanzhen Sun ◽  
Shuiyong Fan ◽  
Xiang-Yu Huang
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Convective Available Potential Energy ◽  
Three Dimensional ◽  
Heavy Rain ◽  
Cloud Water ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Convective System

AbstractAn indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Z–qr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.

scholarly journals Multiscale Mountain Waves Influencing a Major Orographic Precipitation Event

2007 ◽  
Vol 64 (3) ◽  
pp. 711-737 ◽  
Author(s):  
Matthew F. Garvert ◽  
Bradley Smull ◽  
Cliff Mass
Keyword(s):  
Mesoscale Model ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Small Scale ◽  
Second Phase ◽  
Mountain Waves ◽  
Low Level ◽  
Orographic Rainfall ◽  
Vertically Propagating ◽  
Horizontal Wavelength

Abstract This study combines high-resolution mesoscale model simulations and comprehensive airborne Doppler radar observations to identify kinematic structures influencing the production and mesoscale distribution of precipitation and microphysical processes during a period of heavy prefrontal orographic rainfall over the Cascade Mountains of Oregon on 13–14 December 2001 during the second phase of the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) field program. Airborne-based radar detection of precipitation from well upstream of the Cascades to the lee allows a depiction of terrain-induced wave motions in unprecedented detail. Two distinct scales of mesoscale wave–like air motions are identified: 1) a vertically propagating mountain wave anchored to the Cascade crest associated with strong midlevel zonal (i.e., cross barrier) flow, and 2) smaller-scale (<20-km horizontal wavelength) undulations over the windward foothills triggered by interaction of the low-level along-barrier flow with multiple ridge–valley corrugations oriented perpendicular to the Cascade crest. These undulations modulate cloud liquid water (CLW) and snow mixing ratios in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), with modeled structures comparing favorably to radar-documented zones of enhanced reflectivity and CLW measured by the NOAA P3 aircraft. Errors in the model representation of a low-level shear layer and the vertically propagating mountain waves are analyzed through a variety of sensitivity tests, which indicated that the mountain wave’s amplitude and placement are extremely sensitive to the planetary boundary layer (PBL) parameterization being employed. The effects of 1) using unsmoothed versus smoothed terrain and 2) the removal of upstream coastal terrain on the flow and precipitation over the Cascades are evaluated through a series of sensitivity experiments. Inclusion of unsmoothed terrain resulted in net surface precipitation increases of ∼4%–14% over the windward slopes relative to the smoothed-terrain simulation. Small-scale waves (<20-km horizontal wavelength) over the windward slopes significantly impact the horizontal pattern of precipitation and hence quantitative precipitation forecast (QPF) accuracy.

scholarly journals Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5

2012 ◽  
Vol 27 (2) ◽  
pp. 438-450 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Statistical Models ◽  
Mesoscale Model ◽  
Gaussian Kernel ◽  
Support Vector ◽  
State University ◽  
Rainfall Prediction ◽  
Fifth Generation ◽  
Shihmen Reservoir ◽  
Reservoir Watershed ◽  
Lag Times

Abstract This study presents two support vector machine (SVM) based models for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet kernel SVMs (WSVMs). A comparison between the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and statistical models, including SVM-based models and linear regressions (regression), was made in terms of performance of rainfall prediction at the Shihmen Reservoir watershed in Taiwan. Data from 73 typhoons affecting the Shihmen Reservoir watershed were included in the analysis. This study designed six attribute combinations with different lag times for the forecast target. The modified RMSE, bias, and estimated threat score (ETS) results were employed to assess the predicted outcomes. Results show that better attribute combinations for typhoon climatologic characteristics and typhoon precipitation predictions occurred at 0-h lag time with modified RMSE values of 0.288, 0.257, and 0.296 in GSVM, WSVM, and the regression, respectively. Moreover, WSVM having average bias and ETS values close to 1.0 gave better predictions than did the GSVM and regression models. In addition, Typhoons Zeb (1998) and Nari (2001) were selected for comparison between the MM5 model output and the developed statistical models. Results showed that the MM5 tended to overestimate the peak and cumulative rainfall amounts while the statistical models were inclined to yield underestimations.

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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