scholarly journals A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain

2008 ◽  
Vol 23 (6) ◽  
pp. 1085-1101 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Covariance Matrix ◽  
Drop Size ◽  
Size Distributions ◽  
Radar Rainfall ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
Drop Size Distributions ◽  
The Impact ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground is quantified toward deriving and understanding the error covariance matrix of these estimates. The focus here is on stratiform precipitation at a resolution of 15 km, which is most relevant for data assimilation onto mesoscale numerical models. In the characterization of the error structure, the following contributions are considered: (i) the individual effect of the range-dependent error (associated with beam broadening and increasing height of radar measurements with range), (ii) the error associated with the transformation from reflectivity to rain rate due to the variability of drop size distributions, and (iii) the interaction of the first two, that is, the term resulting from the cross correlation between the effects of the range-dependent error and the uncertainty related to the variability of drop size distributions (DSDs). For this purpose a large database of S-band radar observations at short range (where reflectivity near the ground is measured and the beam is narrow) is used to characterize the range-dependent error within a simulation framework, and disdrometric measurements collocated with the radar data are used to assess the impact of the variability of DSDs. It is noted that these two sources of error are well correlated in the vicinity of the melting layer as result of the physical processes that determine the density of snow (e.g., riming), which affect both the DSD variability and the vertical profile of reflectivity.

Enhancing the impact of IASI observations through an updated observation-error covariance matrix

2016 ◽  
Vol 142 (697) ◽  
pp. 1767-1780 ◽  
Author(s):  
Niels Bormann ◽  
Massimo Bonavita ◽  
Rossana Dragani ◽  
Reima Eresmaa ◽  
Marco Matricardi ◽  
...  
Keyword(s):  
Covariance Matrix ◽  
Observation Error ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
The Impact

scholarly journals Generalized background error covariance matrix model (GEN_BE v2.0)

2015 ◽  
Vol 8 (3) ◽  
pp. 669-696 ◽  
Author(s):  
G. Descombes ◽  
T. Auligné ◽  
F. Vandenberghe ◽  
D. M. Barker ◽  
J. Barré
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Chemical Species ◽  
Variational Data Assimilation ◽  
Control Variables ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract. The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE allows for a simpler, flexible, robust, and community-oriented framework that gathers methods used by some meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks of different modeling of B and showing some of the new features in data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to implement new control variables. While the generation of the background errors statistics code was first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily applied to other domains of science and chosen to diagnose and model B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

scholarly journals The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment

2018 ◽  
Vol 18 (12) ◽  
pp. 9121-9145 ◽  
Author(s):  
Die Wang ◽  
Scott E. Giangrande ◽  
Mary Jane Bartholomew ◽  
Joseph Hardin ◽  
Zhe Feng ◽  
...  
Keyword(s):  
Drop Size ◽  
Department Of Energy ◽  
Size Distributions ◽  
Wet Season ◽  
Radar Rainfall ◽  
Precipitation Characteristics ◽  
Central Amazonia ◽  
Atmospheric Radiation Measurement ◽  
Drop Size Distributions ◽  
Self Consistency

Abstract. This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind profiler measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (wet or dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program tropical deployments, with distributions favoring higher concentrations of smaller drops than ARM continental examples. Oceanic-type precipitation characteristics are predominantly observed during the Amazon wet seasons. An exploration of the controls on wet season precipitation properties reveals that wind direction, compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those wet season Amazon events having an oceanic character for their precipitation drop size distributions.

scholarly journals Generalized Background Error covariance matrix model (GEN_BE v2.0)

2014 ◽  
Vol 7 (4) ◽  
pp. 4291-4352
Author(s):  
G. Descombes ◽  
T. Auligné ◽  
F. Vandenberghe ◽  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Chemical Species ◽  
Variational Data Assimilation ◽  
Control Variables ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract. The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model to allow for a simpler, flexible, robust, and community-oriented framework that gathers methods used by meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks and showing some of the new features on data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to involve new control variables. While the generation of the background errors statistics code has been first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily extended to other domains of science and be chosen as a testbed for diagnostic and new modeling of B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

scholarly journals The Green Ocean: Precipitation Insights from the GoAmazon2014/5 Experiment

2018 ◽  
Author(s):  
Die Wang ◽  
Scott E. Giangrande ◽  
Mary Jane Bartholomew ◽  
Joseph Hardin ◽  
Zhe Feng ◽  
...  
Keyword(s):  
Drop Size ◽  
Department Of Energy ◽  
Size Distributions ◽  
Wet Season ◽  
Radar Rainfall ◽  
Precipitation Characteristics ◽  
Central Amazonia ◽  
Drop Size Distributions ◽  
Self Consistency ◽  
Radiation Measurements

Abstract. This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind profiler measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (Wet/Dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurements program (ARM) tropical deployments, with distributions favoring higher concentrations of smaller drops than ARM continental examples. Oceanic type precipitation characteristics are predominantly observed during the Amazon Wet seasons. An exploration of the controls on Wet season precipitation properties reveals that wind direction, as compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those Wet season Amazon events having an oceanic character for their precipitation drop size distributions.

The impact of multi-frequency and forced disturbances upon drop size distributions in prilling

2010 ◽  
Vol 65 (11) ◽  
pp. 3474-3484 ◽  
Author(s):  
C.J. Gurney ◽  
M.J.H. Simmons ◽  
V.L. Hawkins ◽  
S.P. Decent
Keyword(s):  
Drop Size ◽  
Size Distributions ◽  
Drop Size Distributions ◽  
The Impact

scholarly journals On the Influence of Raindrop Collision Outcomes on Equilibrium Drop Size Distributions

2012 ◽  
Vol 69 (5) ◽  
pp. 1534-1546 ◽  
Author(s):  
Olivier P. Prat ◽  
Ana P. Barros ◽  
Firat Y. Testik
Keyword(s):  
Drop Size ◽  
Number Concentration ◽  
Drop Diameter ◽  
Size Distributions ◽  
Left Hand ◽  
Dynamical Simulation ◽  
Drop Number ◽  
Raindrop Size ◽  
Drop Size Distributions ◽  
The Impact

Abstract The objective of this study is to evaluate the impact of a new parameterization of drop–drop collision outcomes based on the relationship between Weber number and drop diameter ratios on the dynamical simulation of raindrop size distributions. Results of the simulations with the new parameterization are compared with those of the classical parameterizations. Comparison with previous results indicates on average an increase of 70% in the drop number concentration and a 15% decrease in rain intensity for the equilibrium drop size distribution (DSD). Furthermore, the drop bounce process is parameterized as a function of drop size based on laboratory experiments for the first time in a microphysical model. Numerical results indicate that drop bounce has a strong influence on the equilibrium DSD, in particular for very small drops (<0.5 mm), leading to an increase of up to 150% in the small drop number concentration (left-hand side of the DSD) when compared to previous modeling results without accounting for bounce effects.

Quantitative Evaluation of Polarimetric Estimates from Scanning Weather Radars using a Vertically Pointing Micro Rain Radar

2020 ◽  
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Marc Schleiss
Keyword(s):  
Drop Size ◽  
Limited Attention ◽  
Size Distributions ◽  
Weather Radars ◽  
X Band ◽  
Microphysical Processes ◽  
Drop Size Distributions ◽  
Differential Reflectivity ◽  
The Impact ◽  
Rain Radar

AbstractConventionally, micro rain radars (MRRs) have been used as a tool to calibrate reflectivity from weather radars, estimate the relation between rainfall rate and reflectivity, and study microphysical processes in precipitation. However, limited attention has been given to the reliability of the retrieved drop size distributions DSDs from MRRs. This study sheds more light on this aspect by examining the sensitivity of retrieved DSDs to the assumptions made to map Doppler spectra into size distributions, and investigates the capability of an MRR to assess polarimetric observations from operational weather radars. For that, an MRR was installed near the Cabauw observatory in the Netherlands, between the IDRA X-band radar and the Herwijnen operational C-band radar. The measurements of the MRR from November 2018 to February 2019 were used to retrieve DSDs and simulate horizontal reflectivity Ze, differential reflectivity ZDR, and specific differential phase KDP in rain. Attention is given to the impact of aliased spectra and right-hand side truncation on the simulation of polarimetric variables. From a quantitative assessment, the correlations of Ze and ZDR between the MRR and Herwijnen radar were 0.93 and 0.70, respectively, while those between the MRR and IDRA were 0.91 and 0.69. However, Ze and ZDR from the Herwijnen radar showed slight biases of 1.07 and 0.25 dB. For IDRA, the corresponding biases were 2.67 and -0.93 dB. Our results show that MRR measurements are advantageous to inspect the calibration of scanning radars and validate polarimetric estimates in rain, provided that the DSDs are correctly retrieved and controlled for quality assurance.

A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain. Part II: Scale Dependence

2009 ◽  
Vol 24 (3) ◽  
pp. 800-811 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Strong Dependence ◽  
Large Dataset ◽  
Radar Rainfall ◽  
Sources Of Uncertainty ◽  
Quantitative Precipitation Estimation ◽  
Error Covariance ◽  
Precipitation Estimation ◽  
Grid Points ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground has been recently quantified at a resolution typically used in schemes assimilating rainfall at the ground onto mesoscale models. Here, the contribution of the two most important sources of uncertainty at nonattenuating wavelengths (the range-dependent error and the uncertainty due to the Z–R transformation) and their interaction are studied as a function of the resolution of radar observations. The analysis is carried out using a large dataset of collocated reflectivity profiles from the McGill S-band radar and disdrometric measurements obtained in stratiform rainfall at resolutions of 1 × 1, 5 × 5, and 15 × 15 km2. Results show that the errors affecting radar quantitative precipitation estimation (QPE) have a strong dependence with range, and that their structure is scale dependent. At the analyzed resolutions, QPE errors are significantly correlated in time and over several grid points.

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