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 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.

Benchmarking the Raw Model-Generated Background Forecast in Rapidly Updated Surface Temperature Analyses. Part II: Gridded Benchmark

2020 ◽  
Vol 148 (2) ◽  
pp. 701-717 ◽  
Author(s):  
Thomas M. Hamill ◽  
Michael Scheuerer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Weather Prediction ◽  
Daily Maximum ◽  
Interpolation Procedure ◽  
Potential Applications ◽  
Validation Procedure ◽  
Background Forecast ◽  
Statistical Interpolation ◽  
Better Than

Abstract This is the second part of a series on benchmarking raw 1-h high-resolution numerical weather prediction surface-temperature forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) system. Such 1-h forecasts are commonly used to underpin the background for an hourly updated surface temperature analysis. The benchmark in this article was produced through a gridded statistical interpolation procedure using only surface observations and a diurnally, seasonally dependent gridded surface temperature climatology. The temporally varying climatologies were produced by synthesizing high-resolution monthly gridded climatologies of daily maximum and minimum temperatures over the contiguous United States with yearly and diurnally dependent estimates of the station-based climatologies of surface temperature. To produce a 1-h benchmark forecast, for a given hour of the day, say 0000 UTC, the gridded climatology was interpolated to station locations and then subtracted from the observations. These station anomalies were statistically interpolated to produce the 0000 UTC gridded anomaly. This anomaly pattern was continued for 1 h and added to the 0100 UTC gridded climatology to generate the 0100 UTC gridded benchmark forecast. The benchmark is thus a simple 1-h persistence of the analyzed deviations from the diurnally dependent climatology. Using a cross-validation procedure with July 2015 and August 2018 data, the gridded benchmark provided competitive, relatively unbiased 1-h surface temperature forecasts relative to the HRRR. Benchmark forecasts were lower in error and bias in 2015, but the HRRR system was highly competitive or better than the gridded benchmark in 2018. Implications of the benchmarking results are discussed, as well as potential applications of the simple benchmarking procedure to data assimilation.

A Simple Technique for Creating Regional Composites of Sea Surface Temperature from MODIS for Use in Operational Mesoscale NWP

2010 ◽  
Vol 49 (11) ◽  
pp. 2267-2284 ◽  
Author(s):  
Jason C. Knievel ◽  
Daran L. Rife ◽  
Joseph A. Grim ◽  
Andrea N. Hahmann ◽  
Joshua P. Hacker ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Real Time ◽  
Simple Technique ◽  
Weather Prediction ◽  
Sea Breeze ◽  
Sea Surface ◽  
Horizontal Wind ◽  
Near Surface

Abstract This paper describes a simple technique for creating regional, high-resolution, daytime and nighttime composites of sea surface temperature (SST) for use in operational numerical weather prediction (NWP). The composites are based on observations from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra. The data used typically are available nearly in real time, are applicable anywhere on the globe, and are capable of roughly representing the diurnal cycle in SST. The composites’ resolution is much higher than that of many other standard SST products used for operational NWP, including the low- and high-resolution Real-Time Global (RTG) analyses. The difference in resolution is key because several studies have shown that highly resolved SSTs are important for driving the air–sea interactions that shape patterns of static stability, vertical and horizontal wind shear, and divergence in the planetary boundary layer. The MODIS-based composites are compared to in situ observations from buoys and other platforms operated by the National Data Buoy Center (NDBC) off the coasts of New England, the mid-Atlantic, and Florida. Mean differences, mean absolute differences, and root-mean-square differences between the composites and the NDBC observations are all within tenths of a degree of those calculated between RTG analyses and the NDBC observations. This is true whether or not one accounts for the mean offset between the skin temperatures of the MODIS dataset and the bulk temperatures of the NDBC observations and RTG analyses. Near the coast, the MODIS-based composites tend to agree more with NDBC observations than do the RTG analyses. The opposite is true away from the coast. All of these differences in point-wise comparisons among the SST datasets are small compared to the ±1.0°C accuracy of the NDBC SST sensors. Because skin-temperature variations from land to water so strongly affect the development and life cycle of the sea breeze, this phenomenon was chosen for demonstrating the use of the MODIS-based composite in an NWP model. A simulated sea breeze in the vicinity of New York City and Long Island shows a small, net, but far from universal improvement when MODIS-based composites are used in place of RTG analyses. The timing of the sea breeze’s arrival is more accurate at some stations, and the near-surface temperature, wind, and humidity within the breeze are more realistic.

scholarly journals Altura da Camada de Mistura na Cidade do Rio de Janeiro

1978 ◽  
Vol 2 ◽  
pp. 71-85
Author(s):  
Benedito Crescencio Ribeiro
Keyword(s):  
Surface Temperature ◽  
Temperature Profile ◽  
Minimum Temperature ◽  
Rio De Janeiro ◽  
Vertical Temperature ◽  
Mixing Depth ◽  
Vertical Temperature Profile ◽  
The Mean ◽  
Maximum Surface Temperature

This work describes the methods for determination of the mixing depths at morning and afternoon hours. In over Rio de Janeiro the morning mixing depth is calculated extending a dry adiabat on the "SKEWT - LOGP" diagram from 12 GMT surface temperature to its intersection with vertical temperature profile, observed at the minimum temperature hours. In the afternoon hours a dry adiabat is extended from maximum surface temperature to Its intersection with the 12 GMT observed vertical temperature profile. The mean mixing depth is calculated for each month over the period 1967-1976 through two distinct ways. The results are very well correlated and show a correlation coefficient greater than 0.9. Over Rio de Janeiro the mean maximum mixing depths are heigher in July (1216 m)and lower in october (812 m). The mean mixing depths in the morning hours are heigher in December (633 m) and lower in June (369 m).

scholarly journals Sensitivity of mixed-phase moderately deep convective clouds to parameterisations of ice formation – An ensemble perspective

2020 ◽  
Author(s):  
Annette K. Miltenberger ◽  
Paul R. Field
Keyword(s):  
High Resolution ◽  
Initial Conditions ◽  
Numerical Models ◽  
Weather Prediction ◽  
Mixed Phase ◽  
Ice Formation ◽  
Wave Radiation ◽  
Initial Condition ◽  
Convective Clouds ◽  
Deep Convective Clouds

Abstract. The formation of ice in clouds is an important processes in mixed-phase and ice-phase clouds. Yet, the representation of ice formation in numerical models is highly uncertain. In the last decade several new parameterisations for heterogeneous freezing have been proposed. It is so far unclear what the effect of choosing one parameterisation over another is in the context of numerical weather prediction. We conducted high-resolution simulations (Δx = 250 m) of moderately deep convective clouds (cloud to about ~ −18 °C over the southwestern UK using several formulations of ice formation and compare the resulting changes in cloud field properties to the spread of a initial condition ensemble for the same case. The strongest impact of altering the ice formation representation is found in the hydrometeor number concentration and mass mixing ratio profiles. While change in accumulated precipitation are around 10 %, high precipitation rates (95th percentile) vary by 20. Using different ice formation representations changes the outgoing short-wave radiation by about 2.9 W m−2 averaged over daylight hours. The choice of a particular representation for ice formation has always a smaller impact then omitting heterogeneous ice formation completely. Excluding the representation of the Hallett–Mossop process or altering the heterogeneous freezing parameterisation has an impact of similar magnitude on most cloud macro- and microphysical variables with the exception of the frozen hydrometeor mass mixing ratios and number concentrations. A comparison to the spread of cloud properties in a 10-member high-resolution initial condition ensemble shows that the sensitivity of hydrometeor profiles to the formulation of ice formation processes is larger than sensitivity to initial conditions. In particular, excluding the Hallet–Mossop representation results in profiles clearly different from any in the ensemble. In contrast, the ensemble spread clearly exceeds the changes introduced by using different ice formation representations in accumulated precipitation, precipitation rates, condensed water path, cloud fraction and outgoing radiation fluxes.

scholarly journals On the Vertical Structure of Modeled and Observed Deep Convective Storms: Insights for Precipitation Retrieval and Microphysical Parameterization

2005 ◽  
Vol 44 (12) ◽  
pp. 1866-1884 ◽  
Author(s):  
Jamie L. Smedsmo ◽  
Efi Foufoula-Georgiou ◽  
Venugopal Vuruputur ◽  
Fanyou Kong ◽  
Kelvin Droegemeier
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Future Research ◽  
Surface Precipitation ◽  
Convective Storms ◽  
Precipitation Forecasting ◽  
Radar Echoes ◽  
Microphysical Parameterization

Abstract An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.

scholarly journals Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model

2011 ◽  
Vol 11 (7) ◽  
pp. 20203-20243 ◽  
Author(s):  
A. Seifert ◽  
C. Köhler ◽  
K. D. Beheng
Keyword(s):  
Surface Temperature ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Environmental Parameters ◽  
Numerical Weather Prediction Model ◽  
Near Surface ◽  
Surface Precipitation ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Convective Scale

Abstract. Possible aerosol-cloud-precipitation effects over Germany are investigated using the COSMO model in a convection-permitting configuration close to the operational COSMO-DE. Aerosol effects on clouds and precipitation are modeled by using an advanced two-moment microphysical parameterization taking into account aerosol assumptions for cloud condensation nuclei (CCN) as well as ice nuclei (IN). Simulations of three summer seasons have been performed with various aerosol assumptions, and are analysed regarding surface precipitation, cloud properties, and the indirect aerosol effect on near-surface temperature. We find that the CCN and IN assumptions have a strong effect on cloud properties, like condensate amounts of cloud water, snow and rain as well as on the glaciation of the clouds, but the effects on surface precipitation are – when averaged over space and time – small. This robustness can only be understood by the combined action of microphysical and dynamical processes. On one hand, this shows that clouds can be interpreted as a buffered system where significant changes to environmental parameters, like aerosols, have little effect on the resulting surface precipitation. On the other hand, this buffering is not active for the radiative effects of clouds, and the changes in cloud properties due to aerosol perturbations have a significant effect on radiation and near-surface temperature.

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.

scholarly journals Sensitivity of mixed-phase moderately deep convective clouds to parameterizations of ice formation – an ensemble perspective

2021 ◽  
Vol 21 (5) ◽  
pp. 3627-3642
Author(s):  
Annette K. Miltenberger ◽  
Paul R. Field
Keyword(s):  
High Resolution ◽  
Initial Conditions ◽  
Numerical Models ◽  
Weather Prediction ◽  
Mixed Phase ◽  
Ice Formation ◽  
Wave Radiation ◽  
Initial Condition ◽  
Convective Clouds ◽  
Deep Convective Clouds

Abstract. The formation of ice in clouds is an important processes in mixed-phase and ice-phase clouds. Yet, the representation of ice formation in numerical models is highly uncertain. In the last decade, several new parameterizations for heterogeneous freezing have been proposed. However, it is currently unclear what the effect of choosing one parameterization over another is in the context of numerical weather prediction. We conducted high-resolution simulations (Δx=250 m) of moderately deep convective clouds (cloud top ∼-18 ∘C) over the southwestern United Kingdom using several formulations of ice formation and compared the resulting changes in cloud field properties to the spread of an initial condition ensemble for the same case. The strongest impact of altering the ice formation representation is found in the hydrometeor number concentration and mass mixing ratio profiles. While changes in accumulated precipitation are around 10 %, high precipitation rates (95th percentile) vary by 20 %. Using different ice formation representations changes the outgoing short-wave radiation by about 2.9 W m−2 averaged over daylight hours. The choice of a particular representation for ice formation always has a smaller impact then omitting heterogeneous ice formation completely. Excluding the representation of the Hallett–Mossop process or altering the heterogeneous freezing parameterization has an impact of similar magnitude on most cloud macro- and microphysical variables with the exception of the frozen hydrometeor mass mixing ratios and number concentrations. A comparison to the spread of cloud properties in a 10-member high-resolution initial condition ensemble shows that the sensitivity of hydrometeor profiles to the formulation of ice formation processes is larger than sensitivity to initial conditions. In particular, excluding the Hallett–Mossop representation results in profiles clearly different from any in the ensemble. In contrast, the ensemble spread clearly exceeds the changes introduced by using different ice formation representations in accumulated precipitation, precipitation rates, condensed water path, cloud fraction, and outgoing radiation fluxes.

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