scholarly journals EnKF Assimilation of Satellite-retrieved Cloud Water Path to Improve Tropical Cyclone Rainfall Forecast

2021 ◽  
Vol 27 (3) ◽  
pp. 201-217
Author(s):  
GAO Xiao-yu ◽  
LIN Yan-luan ◽  
YUE Jian
Keyword(s):  
Tropical Cyclone ◽  
Cloud Water ◽  
Water Path ◽  
Rainfall Forecast ◽  
Cloud Water Path

scholarly journals Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Vol 12 (9) ◽  
pp. 3939-3954
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in midlatitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days are still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure and on the impact of this method on short-term forecasts of GHI for Réunion Island, a tropical island in the southwest Indian Ocean. The multilayer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) Manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated, and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and middle troposphere. Ground-based GHI observations at 12 sites on Réunion Island are used to quantify the impact of CWP DA. Over a total of 44 d during austral summertime, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 h. Root mean square error and mean absolute error are reduced by 4 % and 3 %, respectively.

scholarly journals Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances

2015 ◽  
Vol 8 (7) ◽  
pp. 2663-2683 ◽  
Author(s):  
M. D. Fielding ◽  
J. C. Chiu ◽  
R. J. Hogan ◽  
G. Feingold ◽  
E. Eloranta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Microwave Radiometer ◽  
Cloud Water ◽  
Radar Reflectivity ◽  
New Method ◽  
Error Statistics ◽  
Water Path ◽  
Boundary Layer Clouds ◽  
Cloud Water Path

Abstract. Active remote sensing of marine boundary-layer clouds is challenging as drizzle drops often dominate the observed radar reflectivity. We present a new method to simultaneously retrieve cloud and drizzle vertical profiles in drizzling boundary-layer clouds using surface-based observations of radar reflectivity, lidar attenuated backscatter, and zenith radiances under conditions when precipitation does not reach the surface. Specifically, the vertical structure of droplet size and water content of both cloud and drizzle is characterised throughout the cloud. An ensemble optimal estimation approach provides full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from large-eddy simulation snapshots of cumulus under stratocumulus, where cloud water path is retrieved with an error of 31 g m−2. The method also performs well in non-drizzling clouds where no assumption of the cloud profile is required. We then apply the method to observations of marine stratocumulus obtained during the Atmospheric Radiation Measurement MAGIC deployment in the Northeast Pacific. Here, retrieved cloud water path agrees well with independent three-channel microwave radiometer retrievals, with a root mean square difference of 10–20 g m−2.

scholarly journals Discriminating raining from non-raining clouds at mid-latitudes using meteosat second generation daytime data

2008 ◽  
Vol 8 (9) ◽  
pp. 2341-2349 ◽  
Author(s):  
B. Thies ◽  
T. Nauss ◽  
J. Bendix
Keyword(s):  
Cloud Water ◽  
Convective Precipitation ◽  
Rain Area ◽  
Water Path ◽  
Vertical Extension ◽  
Frontal Systems ◽  
Cloud Water Path ◽  
Precipitating Clouds ◽  
Cloud Top Temperature ◽  
Supply Information

Abstract. A new method for the delineation of precipitation during daytime using multispectral satellite data is proposed. The approach is not only applicable to the detection of mainly convective precipitation by means of the commonly used relation between infrared cloud top temperature and rainfall probability but enables also the detection of stratiform precipitation (e.g. in connection with mid-latitude frontal systems). The presented scheme is based on the conceptual model that precipitating clouds are characterized by a combination of particles large enough to fall, an adequate vertical extension (both represented by the cloud water path; cwp), and the existence of ice particles in the upper part of the cloud. The technique considers the VIS0.6 and the NIR1.6 channel to gain information about the cloud water path. Additionally, the brightness temperature differences ΔT8.7-10.8 and ΔT10.8-12.1 are considered to supply information about the cloud phase. Rain area delineation is realized by using a minimum threshold of the rainfall confidence. To obtain a statistical transfer function between the rainfall confidence and the four parameters VIS

Comparison of in situ cirrus cloud water path and optical depth measurements with MODIS retrievals

2004 ◽  
Author(s):  
Gerald G. Mace ◽  
Ryan Riveland ◽  
Sally Benson ◽  
Steven E. Platnick ◽  
Linnea Avallone ◽  
...  
Keyword(s):  
Optical Depth ◽  
Cloud Water ◽  
Cirrus Cloud ◽  
Water Path ◽  
Cloud Water Path

scholarly journals Joint Cloud Water Path and Rain Water Path Retrievals from ORACLES Observations

2020 ◽  
Author(s):  
Andrew M. Dzambo ◽  
Tristan L'Ecuyer ◽  
Kenneth Sinclair ◽  
Bastiaan van Diedenhoven ◽  
Siddhant Gupta ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Cloud Water ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Surface Precipitation ◽  
Uncertainty Estimates ◽  
Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar-3rd generation (APR-3), passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ~ 10–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP) with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e. rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in-situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud-precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

scholarly journals Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

2019 ◽  
Author(s):  
Frederik Kurzrock ◽  
Hannah Nguyen ◽  
Jerome Sauer ◽  
Fabrice Chane Ming ◽  
Sylvain Cros ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Cloud Water ◽  
Specific Humidity ◽  
Reunion Island ◽  
Short Term ◽  
Water Path ◽  
Réunion Island ◽  
Cloud Water Path ◽  
The Impact

Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in mid-latitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days is still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure, and on the impact of this method on short-term forecasts of GHI for Reunion Island, a tropical island in the South-West Indian Ocean. The multi-layer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and mid troposphere. Ground-based GHI observations at 12 sites on Reunion Island are used to quantify the impact of CWP DA. Over a total of 44 days during austral summer time, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 hours. Root Mean Squared Error and Mean Absolute Error are reduced by 4 % and 3 % respectively.

scholarly journals Evaluation of a Forward Operator to Assimilate Cloud Water Path into WRF-DART

2013 ◽  
Vol 141 (7) ◽  
pp. 2272-2289 ◽  
Author(s):  
Thomas A. Jones ◽  
David J. Stensrud ◽  
Patrick Minnis ◽  
Rabindra Palikonda
Keyword(s):  
Cloud Water ◽  
Cloud Base ◽  
Limited Attention ◽  
Water Path ◽  
Cloud Properties ◽  
Oklahoma Mesonet ◽  
Cloud Water Path ◽  
Mean Square Difference ◽  
The Impact ◽  
Forward Operator

Abstract Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH). Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.

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