Data validation and mesoscale assimilation of Himawari-8 optimal cloud analysis products

2020 ◽  
Author(s):  
Michiko Otsuka ◽  
Hiromu Seko ◽  
Masahiro Hayashi ◽  
Ko Koizumi
Keyword(s):  
Water Vapor ◽  
Initial Conditions ◽  
Effective Radius ◽  
Data Sets ◽  
Spatiotemporal Resolution ◽  
Cloud Properties ◽  
Nonhydrostatic Model ◽  
Vapor Distribution ◽  
Data Assimilation System ◽  
Cloud Analysis

AbstractHimawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of data sets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to mid levels. Next, pseudo-relative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model-based Variational Data Assimilation System. Assimilation of OCA pseudo-relative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

scholarly journals Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

scholarly journals Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System

1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

2014 ◽  
Vol 14 (16) ◽  
pp. 8389-8401 ◽  
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Warm Cloud ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

scholarly journals The Role of Vortex and Environment Errors in Genesis Forecasts of Hurricanes Danielle and Karl (2010)

2013 ◽  
Vol 141 (1) ◽  
pp. 232-251 ◽  
Author(s):  
Ryan D. Torn ◽  
David Cook
Keyword(s):  
Water Vapor ◽  
Vortex Structure ◽  
Wind Shear ◽  
Initial Conditions ◽  
Vertical Wind Shear ◽  
Environmental Parameters ◽  
Empirical Orthogonal Functions ◽  
Vertical Wind ◽  
Orthogonal Functions ◽  
Water Vapor Mixing Ratio

Abstract An ensemble of Weather Research and Forecasting Model (WRF) forecasts initialized from a cycling ensemble Kalman filter (EnKF) system is used to evaluate the sensitivity of Hurricanes Danielle and Karl’s (2010) genesis forecasts to vortex and environmental initial conditions via ensemble sensitivity analysis. Both the Danielle and Karl forecasts are sensitive to the 0-h circulation associated with the pregenesis system over a deep layer and to the temperature and water vapor mixing ratio within the vortex over a comparatively shallow layer. Empirical orthogonal functions (EOFs) of the 0-h ensemble kinematic and thermodynamic fields within the vortex indicate that the 0-h circulation and moisture fields covary with one another, such that a stronger vortex is associated with higher moisture through the column. Forecasts of the pregenesis system intensity are only sensitive to the leading mode of variability in the vortex fields, suggesting that only specific initial condition perturbations associated with the vortex will amplify with time. Multivariate regressions of the vortex EOFs and environmental parameters believed to impact genesis suggest that the Karl forecast is most sensitive to the vortex structure, with smaller sensitivity to the upwind integrated water vapor and 200–850-hPa vertical wind shear magnitude. By contrast, the Danielle forecast is most sensitive to the vortex structure during the first 24 h, but is more sensitive to the 200-hPa divergence and vertical wind shear magnitude at longer forecast hours.

scholarly journals Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China

2017 ◽  
Vol 21 (11) ◽  
pp. 5805-5821 ◽  
Author(s):  
Fan Yang ◽  
Hui Lu ◽  
Kun Yang ◽  
Jie He ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Variations ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Precipitation Data ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

scholarly journals An evaluation of the SAGE III Version 4 aerosol extinction coefficient and water vapor data products

2009 ◽  
Vol 9 (5) ◽  
pp. 22177-22222
Author(s):  
L. W. Thomason ◽  
J. R. Moore ◽  
M. C. Pitts ◽  
J. M. Zawodny ◽  
E.-W. Chiou
Keyword(s):  
Water Vapor ◽  
Extinction Coefficient ◽  
Stratospheric Aerosol ◽  
Data Sets ◽  
Aerosol Extinction ◽  
Data Products ◽  
Mean Differences ◽  
Aerosol Extinction Coefficient ◽  
Polar Ozone

Abstract. Herein, we provide an assessment of the data quality of Stratospheric Aerosol and Gas Experiment (SAGE III) Version 4 aerosol extinction coefficient and water vapor data products. The evaluation is based on comparisons with data from four instruments: SAGE II, the Polar Ozone and Aerosol Measurement (POAM III), the Halogen Occultation Experiment (HALOE), and the Microwave Limb Sounder (MLS). Since only about half of the SAGE III channels have a direct comparison with measurements by other instruments, we have employed some empirical techniques to evaluate measurements at some wavelengths. We find that the aerosol extinction coefficient measurements at 449, 520, 755, 869, and 1021 nm are reliable with accuracies and precisions on the order of 10% in the primary aerosol range of 15 to 25 km. We also believe this to be true of the aerosol measurements at 1545 nm though we cannot exclude some positive bias below 15 km. We recommend use of the 385 nm measurements above 16 km where the accuracy is on par with other aerosol channels. The 601 nm measurement is much noisier (~20%) than other channels and we suggest caution in the use of these data. We believe that the 676 nm data are clearly defective particularly above 20 km (accuracy as poor as 50%) and the precision is also low (~30%). We suggest excluding this channel under most circumstances. The SAGE III Version 4 water vapor data product appears to be high quality and is recommended for science applications in the stratosphere below 45 km. In this altitude range, the mean differences with all four corroborative data sets are no bigger than 15% and often less than 10% with exceptional agreement with POAM III and MLS. Above 45 km, it seems likely that SAGE III water vapor values are increasingly too large and should be used cautiously or avoided. We believe that SAGE III meets its preflight goal of 15% accuracy and 10% precision between 15 and 45 km. We do not currently recommend limiting the SAGE III water vapor data utility in the stratosphere by aerosol loading.

scholarly journals High-Resolution (2.5 km) Ensemble Precipitation Analysis across Canada

2020 ◽  
Vol 21 (9) ◽  
pp. 2023-2039
Author(s):  
Dikra Khedhaouiria ◽  
Stéphane Bélair ◽  
Vincent Fortin ◽  
Guy Roy ◽  
Franck Lespinas
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Background Field ◽  
Spatiotemporal Resolution ◽  
Stochastic Perturbations ◽  
Multiple Observations ◽  
Uncertainty Estimates ◽  
Precipitation Analysis ◽  
Data Assimilation System ◽  
Assimilation System

AbstractConsistent and continuous fields provided by precipitation analyses are valuable for hydrometeorological applications and land data assimilation modeling, among others. Providing uncertainty estimates is a logical step in the analysis development, and a consistent approach to reach this objective is the production of an ensemble analysis. In the present study, a 6-h High-Resolution Ensemble Precipitation Analysis (HREPA) was developed for the domain covering Canada and the northern part of the contiguous United States. The data assimilation system is the same as the Canadian Precipitation Analysis (CaPA) and is based on optimal interpolation (OI). Precipitation from the Canadian national 2.5-km atmospheric prediction system constitutes the background field of the analysis, while at-site records and radar quantitative precipitation estimates (QPE) compose the observation datasets. By using stochastic perturbations, multiple observations and background field random realizations were generated to subsequently feed the data assimilation system and provide 24 HREPA members plus one control run. Based on one summer and one winter experiment, HREPA capabilities in terms of bias and skill were verified against at-site observations for different climatic regions. The results indicated HREPA’s reliability and skill for almost all types of precipitation events in winter, and for precipitation of medium intensity in summer. For both seasons, HREPA displayed resolution and sharpness. The overall good performance of HREPA and the lack of ensemble precipitation analysis (PA) at such spatiotemporal resolution in the literature motivate further investigations on transitional seasons and more advanced perturbation approaches.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signal in ground-based lidar measurements

2014 ◽  
Vol 14 (7) ◽  
pp. 8963-8996
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Background Signal ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signal that is previously unwanted and needs to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds, while it increases with optical depth and reaches an asymptote of 10 μm in non-drizzling clouds. This asymptotic behaviour in non-drizzling clouds is found in both droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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