scholarly journals Convective-Scale Sampling Error and Its Impact on the Ensemble Radar Data Assimilation System: A Case Study of a Heavy Rainfall Event on 16 June 2008 in Taiwan

2020 ◽  
Vol 148 (9) ◽  
pp. 3631-3652
Author(s):  
Pin-Ying Wu ◽  
Shu-Chih Yang ◽  
Chih-Chien Tsai ◽  
Hsiang-Wen Cheng
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Sampling Error ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Observation Error ◽  
Sampling Errors ◽  
Precipitation Prediction ◽  
Convective Scale ◽  
Assimilation System

ABSTRACT Sampling error stems from the use of ensemble-based data assimilation (EDA) with a limited ensemble size and can result in spurious background error covariances, leading to false analysis corrections. The WRF-LETKF radar assimilation system (WLRAS) is performed separately with 256 and 40 members to investigate the characteristics of convective-scale sampling errors in the EDA and its impact on precipitation prediction based on a heavy rainfall event on 16 June 2008. The results suggest that the sampling errors for this event are sensitive to the relationships between the simulated observations and model variables, the intensity of reflectivity, and how the prevailing wind projects to the radial wind in the areas that the radar cannot resolve U or V wind. The sampling errors lead to an underprediction of heavy rainfall when the horizontal localization radius is inadequately large, but this can be mitigated when a more accurate moisture condition is provided. In addition, being able to use a larger vertical localization plays an important role in providing necessary adjustments for representing the vertical thermodynamical structure of convection, which further improves precipitation prediction. A strategy mitigating the impact of sampling errors associated with the limitation of radial wind measurement by inflating the observation error over sensitive areas can bring benefits to precipitation prediction.

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

scholarly journals A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Ashish Routray ◽  
Krishna K. Osuri ◽  
Makarand A. Kulkarni
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Three Dimensional ◽  
Correlation Coefficients ◽  
Rainfall Event ◽  
Initial Time ◽  
Heavy Rainfall Event ◽  
Synoptic Features ◽  
Wrf Modeling ◽  
Data Assimilation Technique

The present study focuses on the performance-based comparison of simulations carried out using nudging (NUD) technique and three-dimensional variational (3DVAR) data assimilation system (3DV) of a heavy rainfall event occurred during 25–28 June 2005 along the west coast of India. The Indian conventional and nonconventional observations are used in the 3DV experiment. Three numerical experiments are conducted using WRF modeling system, the model is integrated upto 54 hours from the initial time 0000 UTC of 25 June 2005. It is noticed that the meteorological parameters are improved in the resulting high-resolution analyses prepared by NUD and 3DV compared to without data assimilation experiment (i.e., called CNTL experiment). However, after the successful inclusion of observations using the 3DVAR data assimilation technique, the model is able to simulate better structure of the convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC) than the NUD experiment and well correlated with the observations. The simulated location and intensity of rainfall is also improved in 3DV simulation as compared with other experiments. Similar results are noticed in the root mean squar errors, correlation coefficients, and Equitable Threat Scores between TRMM and model simulated rainfall for all the three experiments.

scholarly journals Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

2017 ◽  
Author(s):  
Ross Noel Bannister ◽  
Stefano Migliorini ◽  
Alison Clare Rudd ◽  
Laura Hart Baker
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Sampling Error ◽  
Forecast Error ◽  
Significant Degree ◽  
Limited Area ◽  
Ensemble Size ◽  
Sampling Errors ◽  
Convective Scale

Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is large enough to be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains. The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts. It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation purposes.

scholarly journals A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area

2011 ◽  
Vol 139 (6) ◽  
pp. 1911-1931 ◽  
Author(s):  
Takuya Kawabata ◽  
Tohru Kuroda ◽  
Hiromu Seko ◽  
Kazuo Saito
Keyword(s):  
Data Assimilation ◽  
Metropolitan Area ◽  
Heavy Rainfall ◽  
Rainfall Event ◽  
Radar Reflectivity ◽  
Heavy Rainfall Event ◽  
Convective System ◽  
Single Observation ◽  
Assimilation Experiment ◽  
Reflectivity Data

Abstract A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity. The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity. An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

Assimilation of GPS Radio Occultation Refractivity Data with WRF 3DVAR and Its Impact on the Prediction of a Heavy Rainfall Event

2014 ◽  
Vol 53 (6) ◽  
pp. 1381-1398 ◽  
Author(s):  
Ji-Hyun Ha ◽  
Gyu-Ho Lim ◽  
Suk-Jin Choi
Keyword(s):  
Data Assimilation ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Radio Occultation ◽  
Rainfall Event ◽  
Moisture Distribution ◽  
Precipitation Forecast ◽  
Specific Humidity ◽  
Heavy Rainfall Event ◽  
Gps Radio Occultation

AbstractTo accommodate accurate analyses and forecasts of a heavy rainfall event over the Korean Peninsula, the authors assimilated the GPS radio occultation (RO) data by using the Weather Research and Forecasting Model (WRF) and its three-dimensional variational data assimilation system (3DVAR). The employed datasets are from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Challenging Minisatellite Payload (CHAMP) missions. The selected case was from late October 2006, which intensively hit the northeastern part of the Korean Peninsula with record-breaking rainfall. In this study, the local refractivity observation operator was used in assimilating GPS RO soundings. The results are more pronounced for the cycling assimilation of GPS RO data than for the one-time data assimilation. From all of the parameters investigated (temperature, geopotential height, specific humidity, and winds), the GPS RO soundings highly modified the moisture distribution in the lower troposphere and also changed the wind field via the model dynamics. For the heavy rainfall forecast, the quantitative accuracy of the precipitation forecast with the GPS RO data assimilation was in good agreement with observations in terms of the maximum rainfall amount and threat scores. The improved forecast in the experiment came from the exact positioning of the low pressure system and its consequent convergence near the rainfall area. When RO data and GPS precipitable water data were assimilated simultaneously, the moisture distribution changed horizontally and vertically such that it increased the amount of rainfall, and an accurate description of the convective system development was feasible.

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