Search Results
You are looking at 1 - 6 of 6 items for :
- Author or Editor: Wen Chen x
- Journal of Atmospheric and Oceanic Technology x
- Refine by Access: All Content x
Impacts of Instrument Limitations on Estimated Raindrop Size Distribution, Radar Parameters, and Model Microphysics during Mei-Yu Season in East ChinaAbstract
Instrumentation limitations on measured raindrop size distributions (DSDs) and their derived relations and physical parameters are studied through a comparison of the DSD measurements during mei-yu season in east China by four collocated instruments, that is, a two-dimensional video disdrometer (2DVD), a vertically pointing Micro Rain Radar (MRR), and two laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometers (first generation: OTT-1; second generation: OTT-2). Among the four instruments, the 2DVD provides the most accurate DSD and drop velocity measurements, so its measured rainfall amount has the best agreement with the reference rain gauge. Other instruments tend to miss more small drops (D < 1 mm), leading to inaccurate DSDs and a lower rainfall amount. The low rainfall estimation becomes significant during heavy rainfall. The impacts of instrument limitations on the microphysical processes (e.g., evaporation and accretion rates) and convective storm morphology are evaluated. This is important especially for mei-yu precipitation, which is dominated by a high concentration of small drops. Hence, the instrument limitations need to be taken into account in both QPE and microphysics parameterization.
Abstract
Instrumentation limitations on measured raindrop size distributions (DSDs) and their derived relations and physical parameters are studied through a comparison of the DSD measurements during mei-yu season in east China by four collocated instruments, that is, a two-dimensional video disdrometer (2DVD), a vertically pointing Micro Rain Radar (MRR), and two laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometers (first generation: OTT-1; second generation: OTT-2). Among the four instruments, the 2DVD provides the most accurate DSD and drop velocity measurements, so its measured rainfall amount has the best agreement with the reference rain gauge. Other instruments tend to miss more small drops (D < 1 mm), leading to inaccurate DSDs and a lower rainfall amount. The low rainfall estimation becomes significant during heavy rainfall. The impacts of instrument limitations on the microphysical processes (e.g., evaporation and accretion rates) and convective storm morphology are evaluated. This is important especially for mei-yu precipitation, which is dominated by a high concentration of small drops. Hence, the instrument limitations need to be taken into account in both QPE and microphysics parameterization.
A Multiple Linear Regression Algorithm for Sea Surface Temperature Retrieval by One-Dimensional Synthetic Aperture Microwave RadiometryAbstract
One-dimensional synthetic aperture microwave radiometer (1D-SAMR) can provide remote sensing images at a higher spatial resolution than those from traditional real aperture microwave radiometers. As 1D-SAMR operates at multiple incidence angles, we proposed a multiple linear regression method to retrieve sea surface temperature at an incidence angle between 0° and 65°. Assuming that a 1D-SAMR operates at various frequencies (i.e., 6.9, 10.65, 18.7, 23.8 and 36.5 GHz), a radiation transmission forward model was developed to simulate the brightness temperature measured by the 1D-SAMR. The sensitivity of the five frequencies to sea surface temperature was examined, and we evaluated the reliability of the regression method proposed in this study. Furthermore, 11 schemes with various frequency combinations were applied to retrieve sea surface temperature. The results showed that the five-frequency combination scheme performed better than the other schemes. This study also found that the accuracy of retrieved sea surface temperature is dependent on incidence angles. Finally, we suggested that the incidence angle range of the 1D-SAMR is necessary to be 30°–60° based on the relationship between the accuracy of retrieved sea surface temperature and the incidence angles.
Abstract
One-dimensional synthetic aperture microwave radiometer (1D-SAMR) can provide remote sensing images at a higher spatial resolution than those from traditional real aperture microwave radiometers. As 1D-SAMR operates at multiple incidence angles, we proposed a multiple linear regression method to retrieve sea surface temperature at an incidence angle between 0° and 65°. Assuming that a 1D-SAMR operates at various frequencies (i.e., 6.9, 10.65, 18.7, 23.8 and 36.5 GHz), a radiation transmission forward model was developed to simulate the brightness temperature measured by the 1D-SAMR. The sensitivity of the five frequencies to sea surface temperature was examined, and we evaluated the reliability of the regression method proposed in this study. Furthermore, 11 schemes with various frequency combinations were applied to retrieve sea surface temperature. The results showed that the five-frequency combination scheme performed better than the other schemes. This study also found that the accuracy of retrieved sea surface temperature is dependent on incidence angles. Finally, we suggested that the incidence angle range of the 1D-SAMR is necessary to be 30°–60° based on the relationship between the accuracy of retrieved sea surface temperature and the incidence angles.
Quality Control Program for Real-Time Hourly Temperature Observation in TaiwanAbstract
This paper introduces a quality control (QC) program for the real-time hourly land surface temperature observation developed by the Central Weather Bureau in Taiwan. There are three strategies involved. The first strategy is a range check scheme that inspects whether the observation falls inside the climatological limits of the station to screen out the obvious outliers. Limits are adjusted according to the station’s elevation. The second strategy is a spatial check scheme that scrutinizes whether the observation falls inside the derived confidence interval, according to the data from the reference stations and the correlations among the stations, to judge the reliability of the data. The scheme is specialized, as it employs the theorems of unbiased and minimum error estimators to determine the weights. The performance evaluation results show that the new method is in theory superior to the spatial regression test (You et al.). The third strategy is a temporal check scheme that examines whether the temperature difference of two successive observations exceeds the temperature variation threshold for judging the rationality of the data. Different thresholds are applied for the data observed in different times under different rainfall conditions. Procedurally, the observation must pass the range check first and then go through the spatial or the temporal check. The temporal check is applied only when the spatial check is unavailable. Post-examinations of the data from 2014 show that the QC program is able to filter out most of the significant errors.
Abstract
This paper introduces a quality control (QC) program for the real-time hourly land surface temperature observation developed by the Central Weather Bureau in Taiwan. There are three strategies involved. The first strategy is a range check scheme that inspects whether the observation falls inside the climatological limits of the station to screen out the obvious outliers. Limits are adjusted according to the station’s elevation. The second strategy is a spatial check scheme that scrutinizes whether the observation falls inside the derived confidence interval, according to the data from the reference stations and the correlations among the stations, to judge the reliability of the data. The scheme is specialized, as it employs the theorems of unbiased and minimum error estimators to determine the weights. The performance evaluation results show that the new method is in theory superior to the spatial regression test (You et al.). The third strategy is a temporal check scheme that examines whether the temperature difference of two successive observations exceeds the temperature variation threshold for judging the rationality of the data. Different thresholds are applied for the data observed in different times under different rainfall conditions. Procedurally, the observation must pass the range check first and then go through the spatial or the temporal check. The temporal check is applied only when the spatial check is unavailable. Post-examinations of the data from 2014 show that the QC program is able to filter out most of the significant errors.
Uncertainty in Retrieving Raindrop Size Distribution from Polarimetric Radar MeasurementsAbstract
Drop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (K DP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.
Abstract
Drop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (K DP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.
Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational ApproachAbstract
Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (K DP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (Z H) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.
Abstract
Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (K DP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (Z H) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.
Land Surface Temperature from GOES-East and GOES-WestABSTRACT
Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).
ABSTRACT
Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).
