Experimental Test of the Effects of Z R Law Variations on Comparison of WSR-88D Rainfall Amounts with Surface Rain Gauge and Disdrometer Data
|
|
- Lillian Carson
- 5 years ago
- Views:
Transcription
1 JUNE 2001 NOTES AND CORRESPONDENCE 369 Experimental Test of the Effects of Z R Law Variations on Comparison of WSR-88D Rainfall Amounts with Surface Rain Gauge and Disdrometer Data CARLTON W. ULBRICH Department of Physics and Astronomy, Clemson University, Clemson, South Carolina NEIL E. MILLER Science Department, Lander University, Greenwood, South Carolina 26 June 2000 ABSTRACT Reflectivity factors and rainfall rates found from Level II WSR-88D data for the National Weather Service (NWS) radar in Greer, South Carolina (KGSP), are compared with similar parameters found from disdrometer data collected at the Clemson Atmospheric Research Laboratory. These comparisons are used to determine experimentally the sensitivity of rainfall amounts found from the WSR-88D data to variations in the parameters A and b of the Z R law (Z AR b ) that is used in analysis of the data. Analyses of data for nine storms in upstate South Carolina are described. These nine cases encompass a variety of rainfall types including stratiform rain, airmass thunderstorms, and strong cold front convective activity. It is found, after correction of the radar reflectivity factors for obvious calibration offset, that the rainfall depths found by radar are in good agreement with those found from the disdrometer when the NWS default values of A and b (A, b 1.4) are used. If the values of A and b found from an empirical Z R analysis of the disdrometer data are used, then, as expected, the agreement is even better. It is important to recognize that this good agreement was obtained only after adjustment of the radar-measured reflectivity factors for calibration offset. A similar analysis is also described for one other storm but with the addition of rain gauge data from locations in upstate South Carolina, which are remote from the Clemson laboratory and where there are no collocated disdrometers. The radar data are used to determine rainfall amounts for each of the rain gauge locations as well as for the Clemson laboratory. It is found that the agreement between radar-measured rainfall amounts and those found from the rain gauges is very good, even when the default values of A and b are used. Again, this agreement is obtained only after correction of the radar data for the calibration offset found from comparison of the radar reflectivity factors determined from the WSR-88D data and disdrometer data at the Clemson laboratory. When the values of A and b determined from the disdrometer data are used, the agreement is further improved. It is concluded that the commonly observed large differences between KGSP WSR-88D-measured rainfall amounts and surface rain gauge data are not due primarily to variations in Z R law parameters but are the result of hardware calibration offsets. These offsets could probably be eliminated by performing an accurate calibration of the WSR-88D with special emphasis on antenna gain. The results are also of importance to the ground validation field campaigns, which are a part of the Tropical Rainfall Measuring Mission operated by the National Aeronautics and Space Administration. Similar large differences between WSR-88D and surface measurements have been found in these field campaigns. 1. Introduction National Weather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88Ds) estimate rainfall amounts by employing a relationship between radar reflectivity factor Z (mm 6 m 3 ) and the rainfall rate R (mm h 1 ) of the form Z AR b. (1) Corresponding author address: Carlton W. Ulbrich, Department of Physics and Astronomy, Clemson University, Clemson, SC cwulbr@hubcap.clemson.edu The default values of the coefficient A and exponent b used by the NWS are A and b 1.4, which apply to most meterological situations (Hunter 1996). Variations in these parameters are permitted for rain from isolated thunderstorms and for tropical storms. The importance of allowing for such variations has been stressed by Atlas et al. (1999) who show that there can be dramatic changes in Z R law parameters within an individual storm as well as between storms. The latter work also shows that these changes are systematic within storm structure and are clearly identified with the physical processes acting to form rain. Even when such variations in A and b are employed, some NWS radars commonly underestimate rainfall by a factor of 2 or 2001 American Meteorological Society
2 370 WEATHER AND FORECASTING VOLUME 16 more when compared with rain gauge measurements at the surface, especially in stratiform rain (Ulbrich and Lee 1999). Similar differences have been found between WSR-88D cumulative rainfall amounts and those found using the radar, which is a part of the National Aeronautics and Space Administration s Tropical Rainfall Measuring Mission (TRMM; Datta et al. 1999). The purpose of the present work is to assess experimentally the effects of variations in A and b (and thus in the raindrop size distribution) on the accuracy with which the WSR-88D can measure rainfall and to determine the extent to which such variations can account for the large differences observed in the aforementioned experiments. This is done using radar data from the WSR- 88D at the Greer, South Carolina (KGSP), National Weather Service Forecast Office together with disdrometer data from the Clemson Atmospheric Research Laboratory at Clemson University. A comparison is also made of radar-estimated rainfall amounts with those found from rain gauges at three locations in upstate South Carolina. It is found that the radar can estimate rainfall amounts with good accuracy and that the effects of the observed variations in A and b are of minor importance compared to the error due to radar calibration offset. Methods of eliminating such offets are discusssed in Ulbrich and Lee (1999). It must be recognized that the present work does not constitute an effort to validate any exisiting Z R law or to derive a new one to be used in future analyses of WSR-88D data to measure rainfall. This work consists of an analysis of disdrometer and radar data to determine the senstivity of rainfall estimates to variations in A and b and whether such variations can explain the large differences observed between KGSP WSR-88D estimates and surface rain gauge data. 2. Experimental arrangement The location of the disdrometer used in this work is the Clemson Atmospheric Research Laboratory, which is located 60 km to the SW of the KGSP radar and is completely free of any ground clutter surrounding the radar at the lowest radar elevation scan (0.5 ). At this range from the radar the pulse volume is only a few hundred meters above the surface and is well below the freezing level for all the storms considered in this work. Measurements of raindrop size spectra were made using a standard Joss drop disdrometer. The data consist of numbers of raindrops n i of diameter D i in 20 categories ranging in size from 0.35 to 5.25 mm. Computation of the reflectivity factor and rainfall rate from these data involves the simple summations over drop size categories of the form D i i i i 1 Z 10 DN(D ) D and (2) 3 RD 0.6 (D i)din(d i) D i. (3) i 1 These quantities carry the subscript D to indicate that they were determined from disdrometer data. Here, N(D i )(m 3 mm 1 ) is the drop size distribution found from the n i using n i N(D i ), (4) (D i)a Di where (D i )(ms 1 ) is the fall speed in still air of a drop of diameter D i (cm), A (m 2 ) is the area of the disdrometer, (s) is the sampling time, and D i (cm) is the width of the ith category. It is valid to use the fall speed in still air for (D i ) in Eq. (4) since the disdrometer measurements are made at the earth s surface. In this work the sampling time 60 s. A correction was applied to the N(D i ) to account for the dead time associated with the recovery time of the disdrometer s transducer. This correction is described by the manufacturer of the instrument and is summarized by Sauvageot and Lacaux (1995). In this work it was found that there was very little difference between the Z and R values found by the two methods. The conclusions found here are therefore not affected by whether or not this correction is applied. Values of Z measured by the KGSP radar were found from Level II data stored on tape. These values were determined by displaying the lowest (0.5 ) elevation scans at highest resolution and finding the reflectivity factor corresponding to the location of the Clemson laboratory. Careful visual screening of the radar data was performed to avoid observations, which involved highreflectivity gradients near the boundaries of rain areas and other inhomogeneous regions. This was done to find rain areas that were relatively uniform over the location of the disdrometer thereby maximizing the probability that the disdrometer measurements at the surface were representative of that which was measured aloft by the radar. Values of reflectivity factor and rainfall rate determined from such analysis will be refered to henceforth as Z R and R R, the subscript R indicating that they were measured by the radar. For all the storms examined, the temperature at the surface was greater than 60 F during the entire period of observation, thus ensuring that the pulse volume was well below the freezing level. The rain gauge data used in section 4 of this work were obtained from the National Climatic Data Center and are a part of the weather observing sites administered by the National Weather Service. Only data from gauges that made hourly observations were used. 3. Comparisons using disdrometer data In this work radar data for five storms in 1997 and four in 1998 were compared with disdrometer data collected at the Clemson laboratory. A temporal record of the reflectivity factor for one of these storms is shown in Fig. 1a. The solid curve is the record of Z D and the solid circles represent Z R, that is, the reflectivity factor as measured by the radar. It is apparent that the Z R lie
3 JUNE 2001 NOTES AND CORRESPONDENCE 371 FIG. 1. (a) Reflectivity factor Z (dbz) vs time (HHMM) for a storm over the Clemson Atmospheric Research Laboratory on 23 Feb The solid curve depicts Z D as determined from disdrometer data. The solid circles represent Z R, i.e., measurements by the KGSP WSR- 88D. (b) Plot of Z R vs Z D at the same times for the storm in (a). The solid line is the locus of points for which Z R Z D. The dashed line represents a constant offset of Z R below Z D by an amount shown in Table 1 ( 5.4 db). systematically below the Z D, therefore implying that the calibration of the radar is incorrect or that the disdrometer measurements are in error. This is also shown in Fig. 1b, which is a plot of Z R versus Z D. The solid line is a 1:1 line along which all the data would lie if Z R Z D. The dashed line shows the offset of the Z R from the Z D of 5.4 db. The correlation coefficient for these data is 0.97 and the standard deviation is 0.9 db. Very similar results were obtained for all nine storms considered in this section. The average offset for all nine storms is 4.7 db. It is possible to plot all the data for all nine storms on a plot like Fig. 1b; however, a similar plot is shown in Ulbrich and Lee (1999) and is not repeated here. For some of the storms a tipping bucket rain gauge was operated about 10 m from the disdrometer, which produced results for rainfall rates and cumulative depths that were in good agreement with those found from the disdrometer data (Table 1). It is very unlikely then that the disdrometer measurements are significantly in error. In view of the obvious systematic offset of the Z R below the Z D, the effects of variations in the Z R law have been investigated in several ways. First, the total depth of rainfall was determined from the radar data using the default NWS Z R relation, Eq. (1), with A, b 1.4. This was compared with the total depth of rainfall measured by the disdrometer, which is shown in the Table 1 as H (mm). The percent deviation of the radar-measured depth from the disdrometer-measured depth is shown in the column labeled H 1. It is apparent from Table 1 that the use of the default NWS Z R relation with raw Z R data and with no adjustment for possible calibration offset produces rainfall amounts that are a factor of 2 or more too small. In the second method, the rainfall depth was determined using a Z R relation found from standard logarithmic least squares analysis of the Z D and R D disdrometer data acquired at the Clemson laboratory. The latter relation involves a coefficient and exponent A D and b D as shown in Table 1 and was found using only those disdrometer data that lie within the time intervals shown in the table for each date. As before, the radar-measured total depth of rainfall was found using this new Z R relation and the percent deviation of the radar depth from the disdrometer depth is shown in Table 1 as H 2. For all nine storms investigated there is very little or no improvement in the radar-measured rainfall depth TABLE 1. Parameters derived from disdrometer data at the Clemson laboratory and associated radar quantities found from KGSP radar data for the dates and times indicated. Here, N D and N R are the numbers of data points for the disdrometer and radar, respectively: H(mm) the total depth of rainfall measured by the disdrometer; (db) offset of radar Z values from the disdrometer values; A D,b D coefficient and exponent in Z R relation found from the disdrometer data; H 1, H 2 offsets (%) from H of rain depths determined from radar data with (A, b) (, 1.4) and (A, b) (A D,b D ), respectively; H 3, H 4 same as H 1 H 2 but with the offset applied before using the Z R law; H G difference (%) of tipping bucket rain gauge measurement from H; and not available. Date Time (LT) N D N R H A D b D H 1 H 2 H 3 H 4 H G 3 May Jul Nov Nov Dec Jan Jan Feb Feb
4 372 WEATHER AND FORECASTING VOLUME 16 relative to the disdrometer measured depth when use is made of this Z R relation appropriate to the rainfall, that is, when account is made of variation in the Z R law parameters from the default NWS values. In the third and fourth methods the average offset of the radar-measured Z R values from the disdrometer-measured values was found. This value was then used to adjust the radar values of Z R before determining the rainfall depth from the relevant Z R relation. The results found in this way using the default NWS Z R relation and the Z D R D relation for each storm are shown in Table 1as H 3 and H 4, respectively. The values for H 3 demonstrate that the default NWS Z R relation measures rainfall amounts adequately, but only after adjustment for possible calibration offset has been performed. The results shown for H 4 show that some further improvement is possible when a Z R relation is used that is known to apply to the rainfall event under observation. It should be noted that the above analyses do not involve the use of the NWS tropical Z R relation with A 250, b 1.2. It was deemed improper to consider the use of such a relation in this work since all of the storms analyzed were midlatitude in character and none was distinctly tropical in nature. 4. Comparisons using disdrometer and rain gauge data An analysis similar to that described in the previous section was performed using WSR-88D data together with disdrometer and rain gauge data collected on 7 January The principal difference between the earlier analysis and that described here is that rainfall depths were estimated using WSR-88D data over three NWS rain gauges at locations in upstate South Carolina, remote from the Clemson laboratory. These radar-estimated rainfall depths are then compared with those measured by the rain gauges. Each of the three locations used in this work lies at a range from the radar similar to that for the Clemson laboratory (but at different azimuth) thus ensuring that the radar measurements are well below the bright band. The temporal behavior of Z and R for the storm of interest was fairly smooth for the period LT. Some rain fell during an earlier period ( LT) on this day but the behavior of the integral parameters during this period was not as smooth as during the later times. The approximate uniformity of the rainfall field during the period LT was also reflected in the plan position indicator displays of the WSR-88D data. As indicated earlier, choosing a period of approximate uniformity tends to avoid problems associated with mismatch of the radar sampling volume aloft and the disdrometer and rain gauge sampling volumes at the surface. Using the Z D and R D values calculated from the disdrometer data collected at the Clemson laboratory, an empirical Z R analysis was performed for the data FIG. 2. Empirical Z R analysis of disdrometer data for a storm over the Clemson Atmospheric Research Laboratory on 7 Jan collected during LT, which yielded the values shown in Fig. 2, namely, A D, b D. Note that total depth of rain for the specified time period is large (52.7 mm) and that most of the rainfall rates were large. This behavior and the smoothness of the temporal behavior of the integral parameters referred to above tends to avoid problems associated with mismatch of the sampling volumes aloft and at the surface. Almost all of the radar Z R values during the period of interest lie below the disdrometer values and involve an average offset of the radar Z R below the disdrometer Z D of db. This value is similar to that found from analysis of the storms described in the previous section. As a result, the radar values of R R found by assuming A, b 1.4 lie systematically below the disdrometer data and yield a radar estimate of total rainfall depth of 18.8 mm, about 64% below the disdrometer value. If the Z R relation found from the disdrometer data for this day and time period (i.e., A D, b D ) is applied to the radar data, it is found that the radar-estimated rainfall depth is 20.7 mm, a difference from the disdrometer value of 61.7%. The latter result is quite similar to that found with A, b 1.4 thus indicating that use of the empirical values of A D and b D has very little effect on improving the agreement between radar and disdrometer. If the analysis is performed using the default A and b values but with the radar Z R values increased by the offset of 5.1 db, then somewhat better agreement is found between the disdrometer and radar-derived rainfall rates. The total depth of rainfall found by the radar is 43.7 mm, a difference from the disdrometer value of only 17%. Finally, if the analysis is done using the disdrometer values of A D and b D and the calibration offset is also applied, then the results show even better
5 JUNE 2001 NOTES AND CORRESPONDENCE 373 TABLE 2. Summary of disdrometer, rain gauge, and radar-deduced parameters for a rainfall event in South Carolina on 7 Jan 1998 during the time period LT. The first column lists the station name, under which is given the range and compass bearing of the station from the KGSP radar. Here, A and b are the coefficient and exponent in the empirical Z R law used in analysis of the radar data; Z is the offset of the radar Z values from those found from the disdrometer data (db); H R is the value of the rainfall depth as deduced from the radar-measured Z values using the A and b values shown on the same line; H G is the rainfall depth found from the surface instrument (disdrometer for the Clemson station, rain gauge for the other three); and H is the deviation of H R from H G in percent. Station Clemson (60 km, 246 ) Longcreek (95 km, 264 ) Jocassee (78 km, 278 ) Calhoun Falls (95 km, 201 ) Start time (LT) End time (LT) Z H G A b H R H (%) agreement with a total radar-derived depth of rainfall equal to 47.0 mm, a difference of less than 11% from the disdrometer value. Consequently, adjustment of A and b to account for their natural variations in this storm helps to improve the radar estimate but by a minor amount compared to the error due to calibration offset. The results described here are summarized in Table 2, where H R and H D are the total depths of rainfall found using the radar and disdrometer, respectively; H is the difference between H R and H D in percent; Z is the offset of the radar Z values from the disdrometer values (db); and A and b are the values used in the Z R relation in determining the radar-measured rainfall depth. Similar results are obtained when radar-measured rainfall amounts are compared with rain gauge data where no disdrometer is available at the location of the rain gauge. To illustrate this point, three rain gauges in upstate South Carolina have been used that are at locations remote from the Clemson laboratory. All of these gauges are at similar ranges (but different azimuths) from the radar and all radar measurements are well below the bright band. Each involves large and therefore useful amounts of rainfall for this storm. The locations of the gauges are at Jocassee, Longcreek, and Calhoun Falls, South Carolina, and are a part of the network of precipitation gauges supervised by the National Weather Service. Data for these stations were obtained from the National Climatic Data Center. Each of these stations has a gauge that records hourly amounts of precipitation and they are the only gauges in upstate South Carolina within range of the KGSP radar, which made such measurements for this storm. The comparisons of rain gauge amounts with radarmeasured amounts are similar for all three stations. The results for one of the stations (Longcreek) is shown in Fig. 3. The solid line connects hourly measurements made by the rain gauge. The solid circles represent the rainfall amounts determined from the radar data using the Z R method described earlier and with A, b 1.4. The radar Z R values used in this method have been offset or corrected by the radar calibration offset Z db as determined from the disdrometer data acquired at the Clemson laboratory. The agreement between the rainfall amounts determined by the two methods is very good. The radar-estimated cumulative depth of rainfall, as shown in Table 2, differs from the gauge amount by only about 23%. If the values of A D and b D found from analysis of the disdrometer data collected at the Clemson laboratory are used in the analysis, then the difference between radar and rain gauge estimates is only 16%. Similar behavior is found for the other two locations shown in Table 2. It is interesting to note that the agreement between rain gauge and radar amounts for the Calhoun Falls station is better when the default values of A and b 1.4 are used rather than the values determined from the disdrometer data. In summary, this analysis has shown that the KGSP WSR-88D has the potential to measure rainfall amounts with good accuracy. The highest accuracy is obtained by allowing for adjustment of the coefficient A and exponent b in the Z R law for the type of rain being observed. It has demonstrated, however, that in order to achieve this level of accuracy it is necessary to account for any calibration offsets in the radar data that may exist and these have been shown to be the major contributor to the difference between radar and surface measurements. Ulbrich and Lee (1999) discuss methods by which such calibration offsets can be eliminated. The
6 374 WEATHER AND FORECASTING VOLUME 16 FIG. 3. (top) Rainfall depth H vs time at a rain gauge site in upstate SC for a storm on 7 Jan The solid curve represents the depth as measured by a rain gauge administered by NOAA and the solid circles are the cumulative rainfall depth as measured by the KGSP WSR-88D. Here, A, b are the parameters of the Z R law used to determined rainfall rates from the WSR- 88D data; H G and H R are the rainfall depths found from the rain gauge data and from analysis of the radar data, respectively. (bottom) Plot of the radar-measured Z R as a function of time. The offset of db of Z R from Z D as found from analysis of disdrometer data at the Clemson Atmospheric Research Laboratory has been applied to the radar data. most important of these methods is accurate determination of the antenna gain. 5. Conclusions It has been found here that, for the storms investigated in this work, variations in the coefficient A and exponent b in the Z R law used to determine rainfall amounts using WSR-88D data cannot account for the large differences between radar-measured and surface rain gauge measured rainfall observed with the KGSP WSR-88D in Greer. However, it has been shown that dramatic improvement results when adjustment of the measured radar reflectivity factors is performed to allow for possible calibration offset. This has been demonstrated through comparison of disdrometer-measured reflectivity factors with those measured by the KGSP radar. This analysis probably also explains similar large deviations observed with other WSR-88D as well as those found in comparison of SA TRMM precipitation radar data with WSR-88D data. The most accurate method of removing the calibration offset is through hardware calibration of the radar, as discussed by Ulbrich and Lee (1999). Since current NWS calibration procedures involve careful analysis of all components up to the antenna, the most likely source of error is inaccurate determination of antenna gain. Once this factor is determined accurately the performance of the radar can be checked periodically using methods described in this work. Acknowledgments. C. Ulbrich s participation in this work was supported by Contract S with the SA Goddard Space Flight Center. REFERENCES Atlas, D., C. W. Ulbrich, F. D. Marks Jr., E. Amitai, and C. R. Williams, 1999: Systematic variation of drop size and radar-rainfall relations. J. Geophys. Res., 104 (D6), Datta, S., B. Roy, L. Jones, T. Kasparis, P. Ray, and D. Charalampidis, 1999: Evaluation of TRMM precipitation radar rainfall estimates using NEXRAD and rain gauges in central and south Forida. Preprints, 29th Int. Conf. on Radar Meteorology, Montreal, PQ, Canada, Amer. Meteor. Soc., Hunter, S. M., 1996: WSR-88D radar rainfall estimation: Capabilities, limitations and potential improvements. Natl. Wea. Dig., 20, Sauvageot, H., and J.-P. Lacaux, 1995: The shape of averaged drop size distributions. J. Atmos. Sci., 52, Ulbrich, C. W., and L. G. Lee, 1999: Radar measurement error by WSR-88D radars due to variations in Z R law parameters and the radar constant. J. Atmos. Oceanic Technol., 16,
Precipitation estimate of a heavy rain event using a C-band solid-state polarimetric radar
Precipitation estimate of a heavy rain event using a C-band solid-state polarimetric radar Hiroshi Yamauchi 1, Ahoro Adachi 1, Osamu Suzuki 2, Takahisa Kobayashi 3 1 Meteorological Research Institute,
More informationTesting a Polarimetric Rainfall Algorithm and Comparison with a Dense Network of Rain Gauges.
Testing a Polarimetric Rainfall Algorithm and Comparison with a Dense Network of Rain Gauges. Alexander Ryzhkov (1,2), Terry Schuur (1,2), Dusan Zrnic (1) 1 National Severe Storms Laboratory, 1313 Halley
More informationEd Tomlinson, PhD Bill Kappel Applied Weather Associates LLC. Tye Parzybok Metstat Inc. Bryan Rappolt Genesis Weather Solutions LLC
Use of NEXRAD Weather Radar Data with the Storm Precipitation Analysis System (SPAS) to Provide High Spatial Resolution Hourly Rainfall Analyses for Runoff Model Calibration and Validation Ed Tomlinson,
More informationOn the Influence of Assumed Drop Size Distribution Form on Radar-Retrieved Thunderstorm Microphysics
FEBRUARY 2006 B R A N D E S E T A L. 259 On the Influence of Assumed Drop Size Distribution Form on Radar-Retrieved Thunderstorm Microphysics EDWARD A. BRANDES, GUIFU ZHANG, AND JUANZHEN SUN National Center
More information3R.1 USING GROUND CLUTTER TO ADJUST RELATIVE RADAR CALIBRATION AT KWAJALEIN, RMI
3R.1 USING GROUND CLUTTER TO ADJUST RELATIVE RADAR CALIBRATION AT KWAJALEIN, RMI David S. Silberstein 1,2,*, D. B. Wolff 1,3, D. A. Marks 1,2, and J. L. Pippitt 1,2 1 NASA Goddard Space Flight Center,
More information2.12 Inter-Comparison of Real-Time Rain Gage and Radar-Estimated Rainfall on a Monthly Basis for Midwestern United States Counties
2.12 Inter-Comparison of Real-Time Rain and -Estimated Rainfall on a Monthly Basis for Midwestern United States Counties Nancy Westcott* and Kenneth E. Kunkel Illinois State Water Survey Champaign, Illinois
More informationTHE CHARACTERISTICS OF DROP SIZE DISTRIBUTIONS AND CLASSIFICATIONS OF CLOUD TYPES USING GUDUCK WEATHER RADAR, BUSAN, KOREA
THE CHARACTERISTICS OF DROP SIZE DISTRIBUTIONS AND CLASSIFICATIONS OF CLOUD TYPES USING GUDUCK WEATHER RADAR, BUSAN, KOREA Dong-In Lee 1, Min Jang 1, Cheol-Hwan You 2, Byung-Sun Kim 2, Jae-Chul Nam 3 Dept.
More informationNOTES AND CORRESPONDENCE. A Comparison of Radar Reflectivity Estimates of Rainfall from Collocated Radars
1264 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 16 NOTES AND CORRESPONDENCE A Comparison of Radar Reflectivity Estimates of Rainfall from Collocated Radars EDWARD A. BRANDES, J.VIVEKANANDAN,
More informationA ZDR Calibration Check using Hydrometeors in the Ice Phase. Abstract
A ZDR Calibration Check using Hydrometeors in the Ice Phase Michael J. Dixon, J. C. Hubbert, S. Ellis National Center for Atmospheric Research (NCAR), Boulder, Colorado 23B.5 AMS 38 th Conference on Radar
More informationSystematic Variation of Observed Radar Reflectivity Rainfall Rate Relations in the Tropics
2198 JOURNAL OF APPLIED METEOROLOGY Systematic Variation of Observed Radar Reflectivity Rainfall Rate Relations in the Tropics EYAL AMITAI Joint Center for Earth Systems Technology, University of Maryland,
More informationMulti-Sensor Precipitation Reanalysis
Multi-Sensor Precipitation Reanalysis Brian R. Nelson, Dongsoo Kim, and John J. Bates NOAA National Climatic Data Center, Asheville, North Carolina D.J. Seo NOAA NWS Office of Hydrologic Development, Silver
More informationLei Feng Ben Jong-Dao Jou * T. D. Keenan
P2.4 CONSIDER THE WIND DRIFT EFFECTS IN THE RADAR-RAINGAUGE COMPARISONS Lei Feng Ben Jong-Dao Jou * T. D. Keenan National Science and Technology Center for Disaster Reduction, Taipei County, R.O.C National
More informationA Comparison of Tornado Warning Lead Times with and without NEXRAD Doppler Radar
MARCH 1996 B I E R I N G E R A N D R A Y 47 A Comparison of Tornado Warning Lead Times with and without NEXRAD Doppler Radar PAUL BIERINGER AND PETER S. RAY Department of Meteorology, The Florida State
More information1306 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 15
1306 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 15 The Effect of Vertical Air Motions on Rain Rates and Median Volume Diameter Determined from Combined UHF and VHF Wind Profiler Measurements
More informationLab 6 Radar Imagery Interpretation
Lab 6 Radar Imagery Interpretation Background Weather radar (radio detection and ranging) is another very useful remote sensing tool used in meteorological forecasting. Microwave radar was developed in
More informationTHE DETECTABILITY OF TORNADIC SIGNATURES WITH DOPPLER RADAR: A RADAR EMULATOR STUDY
P15R.1 THE DETECTABILITY OF TORNADIC SIGNATURES WITH DOPPLER RADAR: A RADAR EMULATOR STUDY Ryan M. May *, Michael I. Biggerstaff and Ming Xue University of Oklahoma, Norman, Oklahoma 1. INTRODUCTION The
More informationA Field Study of Reflectivity and Z R Relations Using Vertically Pointing Radars and Disdrometers
1120 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 26 A Field Study of Reflectivity and Z R Relations Using Vertically Pointing Radars and Disdrometers ALI TOKAY
More informationImke Durre * and Matthew J. Menne NOAA National Climatic Data Center, Asheville, North Carolina 2. METHODS
9.7 RADAR-TO-GAUGE COMPARISON OF PRECIPITATION TOTALS: IMPLICATIONS FOR QUALITY CONTROL Imke Durre * and Matthew J. Menne NOAA National Climatic Data Center, Asheville, North Carolina 1. INTRODUCTION Comparisons
More informationFundamentals of Radar Display. Atmospheric Instrumentation
Fundamentals of Radar Display Outline Fundamentals of Radar Display Scanning Strategies Basic Geometric Varieties WSR-88D Volume Coverage Patterns Classic Radar Displays and Signatures Precipitation Non-weather
More informationP1.23 HISTOGRAM MATCHING OF ASMR-E AND TMI BRIGHTNESS TEMPERATURES
P1.23 HISTOGRAM MATCHING OF ASMR-E AND TMI BRIGHTNESS TEMPERATURES Thomas A. Jones* and Daniel J. Cecil Department of Atmospheric Science University of Alabama in Huntsville Huntsville, AL 1. Introduction
More informationSystematic Variation of Rain Rate and Radar Reflectivity Relations for Micro Wave Applications in a Tropical Location.
IOSR Journal of Applied Physics (IOSR-JAP) e-issn: 2278-4861.Volume 7, Issue 6 Ver. I (Nov. - Dec. 215), PP 23-29 www.iosrjournals Systematic Variation of Rain Rate and Radar Reflectivity Relations for
More informationQUANTITATIVE PRECIPITATION ESTIMATION AND ERROR ANALYSIS WITH A UHF WIND PROFILING RADAR AND A TWO-DIMENSIONAL VIDEO DISDROMETER
P13B.6 1 QUANTITATIVE PRECIPITATION ESTIMATION AND ERROR ANALYSIS WITH A UHF WIND PROFILING RADAR AND A TWO-DIMENSIONAL VIDEO DISDROMETER Laura M. Kanofsky 1,, Phillip B. Chilson 1, Terry J. Schuur 2,
More informationAn Evaluation of Radar Rainfall Estimates from Specific Differential Phase
363 An Evaluation of Radar Rainfall Estimates from Specific Differential Phase EDWARD A. BRANDES National Center for Atmospheric Research, Boulder, Colorado ALEXANDER V. RYZHKOV AND DUS AN S. ZRNIĆ National
More informationCALIBRATING THE QUIKSCAT/SEAWINDS RADAR FOR MEASURING RAINRATE OVER THE OCEANS
CALIBRATING THE QUIKSCAT/SEAWINDS RADAR FOR MEASURING RAINRATE OVER THE OCEANS David E. Weissman Hofstra University, Hempstead, New York 11549 Mark A. Bourassa COAPS/The Florida State University, Tallahassee,
More informationPolarization Diversity for the National Weather Service (NWS), WSR-88D radars
Polarization Diversity for the National Weather Service (NWS), WSR-88D radars Dusan S. Zrnic National Severe Storm Laboratory Norman, OK 73069, USA In the early eighties the NOAA s National Severe Storms
More informationBrian R. Nelson, Dongsoo Kim, and John J. Bates NOAA National Climatic Data Center, Asheville, North Carolina
4.6 MULTI-SENSOR PRECIPITATION REANALYSIS Brian R. Nelson, Dongsoo Kim, and John J. Bates NOAA National Climatic Data Center, Asheville, North Carolina D.J. Seo NOAA NWS Office of Hydrologic Development,
More informationRaindrop Size Distributions and Z-R Relations in Coastal Rainfall For Periods With and Without a Radar Brightband
American Meteorological Society 11 th Conference on Mesoscale Processes October 2005, Albuquerque, NM JP4J.1 Raindrop Size Distributions and Z-R Relations in Coastal Rainfall For Periods With and Without
More information2.6 HOW MUCH RAIN REACHES THE SURFACE? LESSONS LEARNED FROM VERY HIGH RESOLUTION OBSERVATIONS IN THE GOODWIN CREEK WATERSHED
2.6 HOW MUCH RAIN REACHES THE SURFACE? LESSONS LEARNED FROM VERY HIGH RESOLUTION OBSERVATIONS IN THE GOODWIN CREEK WATERSHED Matthias Steiner and James A. Smith Princeton University, Princeton, NJ Lisa
More informationAn attempt to calibrate the UHF strato-tropospheric radar at Arecibo using NexRad radar and disdrometer data
Annales Geophysicae (2004) 22: 4025 4034 SRef-ID: 1432-0576/ag/2004-22-4025 European Geosciences Union 2004 Annales Geophysicae An attempt to calibrate the UHF strato-tropospheric radar at Arecibo using
More informationEVALUATING THE QUIKSCAT/SEAWINDS RADAR FOR MEASURING RAINRATE OVER THE OCEANS USING COLLOCATIONS WITH NEXRAD AND TRMM
JP2.9 EVALUATING THE QUIKSCAT/SEAWINDS RADAR FOR MEASURING RAINRATE OVER THE OCEANS USING COLLOCATIONS WITH NEXRAD AND TRMM David E. Weissman* Hofstra University, Hempstead, New York 11549 Mark A. Bourassa
More informationSAMPLE. SITE SPECIFIC WEATHER ANALYSIS Rainfall Report. Bevens Engineering, Inc. Susan M. Benedict REFERENCE:
SAMPLE SITE SPECIFIC WEATHER ANALYSIS Rainfall Report PREPARED FOR: Bevens Engineering, Inc. Susan M. Benedict REFERENCE: DUBOWSKI RESIDENCE / FILE# 11511033 CompuWeather Sample Report Please note that
More informationDeveloping a Z-R Relationship with Uniform Sampling. Kate A O Dell. Dr. Michael L Larsen (Mentor)
Generated using version 3.0 of the official AMS LATEX template Developing a Z-R Relationship with Uniform Sampling Kate A O Dell Department of Physics and Astronomy, College of Charleston, Charleston SC
More informationMeteorology 311. RADAR Fall 2016
Meteorology 311 RADAR Fall 2016 What is it? RADAR RAdio Detection And Ranging Transmits electromagnetic pulses toward target. Tranmission rate is around 100 s pulses per second (318-1304 Hz). Short silent
More informationOBSERVATIONS OF WINTER STORMS WITH 2-D VIDEO DISDROMETER AND POLARIMETRIC RADAR
P. OBSERVATIONS OF WINTER STORMS WITH -D VIDEO DISDROMETER AND POLARIMETRIC RADAR Kyoko Ikeda*, Edward A. Brandes, and Guifu Zhang National Center for Atmospheric Research, Boulder, Colorado. Introduction
More informationand hydrological applications
Overview of QPE/QPF techniques and hydrological applications Siriluk Chumchean Department of Civil Engineering Mahanakorn University of Technology Typhoon Committee Roving Seminar 2011, Malaysia (20-23
More informationValidation of the Zero-Covariance Assumption in the Error Variance Separation Method of Radar-Raingauge Comparisons
Validation of the Zero-Covariance Assumption in the Error Variance Separation Method of Radar-Raingauge Comparisons Grzegorz J. Ciach 1, Emad Habib 2 and Witold F. Krajewski 2 1 Environmental Verification
More informationBrady E. Newkirk Iowa State University,
Meteorology Senior Theses Undergraduate Theses and Capstone Projects 12-2016 Rainfall Estimation from X-band Polarimetric Radar and Disdrometer Observation Measurements Compared to NEXRAD Measurements:
More informationSAMPLE. SITE SPECIFIC WEATHER ANALYSIS Rainfall Report. Bevins Engineering, Inc. Susan M. Benedict. July 1, 2017 REFERENCE:
SAMPLE SITE SPECIFIC WEATHER ANALYSIS Rainfall Report PREPARED FOR: Bevins Engineering, Inc. Susan M. Benedict July 1, 2017 REFERENCE: DUBOWSKI RESIDENCE / FILE# 11511033 1500 Water Street, Pensacola,
More informationComparison of Freezing-Level Altitudes from the NCEP Reanalysis with TRMM Precipitation Radar Brightband Data
1DECEMBER 2000 HARRIS ET AL. 4137 Comparison of Freezing-Level Altitudes from the NCEP Reanalysis with TRMM Precipitation Radar Brightband Data GETTYS N. HARRIS JR., KENNETH P. BOWMAN, AND DONG-BIN SHIN
More informationSAMPLE. SITE SPECIFIC WEATHER ANALYSIS Wind Report. Robinson, Smith & Walsh. John Smith REFERENCE:
SAMPLE SITE SPECIFIC WEATHER ANALYSIS Wind Report PREPARED FOR: Robinson, Smith & Walsh John Smith REFERENCE: JACK HIGGINS / 4151559-01 CompuWeather Sample Report Please note that this report contains
More informationCHAPTER 2 VALIDATION OF RAIN RATE ESTIMATION IN HURRICANES FROM THE STEPPED FRQUENCY MICROWAVE RADIOMETER (SFMR)
CHAPTER 2 VALIDATION OF RAIN RATE ESTIMATION IN HURRICANES FROM THE STEPPED FRQUENCY MICROWAVE RADIOMETER (SFMR) ALGORITHM CORRECTION AND ERROR ANALYSIS 2.1 Abstract Simultaneous observations by the Lower
More informationCharacteristics of the Mirror Image of Precipitation Observed by the TRMM Precipitation Radar
VOLUME 19 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY FEBRUARY 2002 Characteristics of the Mirror Image of Precipitation Observed by the TRMM Precipitation Radar JI LI ANDKENJI NAKAMURA Institute for
More informationA Method for Estimating Rain Rate and Drop Size Distribution from Polarimetric Radar Measurements
830 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 4, APRIL 2001 A Method for Estimating Rain Rate and Drop Size Distribution from Polarimetric Radar Measurements Guifu Zhang, J. Vivekanandan,
More informationApplication of Scaling in Radar Reflectivity for Correcting Range-Dependent Bias in Climatological Radar Rainfall Estimates
OCTOBER 2004 CHUMCHEAN ET AL. 1545 Application of Scaling in Radar Reflectivity for Correcting Range-Dependent Bias in Climatological Radar Rainfall Estimates SIRILUK CHUMCHEAN School of Civil and Environmental
More informationIMPROVEMENTS OF POLARIMETRIC RADAR ECHO CLASSIFICATIONS. Ronald Hannesen* Selex-Gematronik, Neuss, Germany
P13.14 IMPROVEMENTS OF POLARIMETRIC RADAR ECHO CLASSIFICATIONS Ronald Hannesen* Selex-Gematronik, Neuss, Germany 1. INTRODUCTION A two-step radar echo classification is applied on polarimetric radar data:
More informationAn Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations
2038 JOURNAL OF APPLIED METEOROLOGY An Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations HONGPING LIU, V.CHANDRASEKAR, AND GANG XU Colorado State University, Fort Collins,
More informationSNOWFALL RATE RETRIEVAL USING AMSU/MHS PASSIVE MICROWAVE DATA
SNOWFALL RATE RETRIEVAL USING AMSU/MHS PASSIVE MICROWAVE DATA Huan Meng 1, Ralph Ferraro 1, Banghua Yan 2 1 NOAA/NESDIS/STAR, 5200 Auth Road Room 701, Camp Spring, MD, USA 20746 2 Perot Systems Government
More informationP. N. Gatlin 1, Walter. A. Petersen 2, Lawrence. D. Carey 1, Susan. R. Jacks Introduction
P14.21 34 th Conference on Radar Meteorology Williamsburg, VA, 4-9 Oct 2009 The NEXRAD Rainfall Estimation Processing System: A radar tool to improve rainfall estimation across the Tennessee River Valley
More informationThe TRMM Precipitation Radar s View of Shallow, Isolated Rain
OCTOBER 2003 NOTES AND CORRESPONDENCE 1519 The TRMM Precipitation Radar s View of Shallow, Isolated Rain COURTNEY SCHUMACHER AND ROBERT A. HOUZE JR. Department of Atmospheric Sciences, University of Washington,
More informationTropical Rainfall Rate Relations Assessments from Dual Polarized X-band Weather Radars
Tropical Rainfall Rate Relations Assessments from Dual Polarized X-band Weather Radars Carlos R. Wah González, José G. Colom Ustáriz, Leyda V. León Colón Department of Electrical and Computer Engineering
More informationThe Integration of WRF Model Forecasts for Mesoscale Convective Systems Interacting with the Mountains of Western North Carolina
Proceedings of The National Conference On Undergraduate Research (NCUR) 2006 The University of North Carolina at Asheville Asheville, North Carolina April 6-8, 2006 The Integration of WRF Model Forecasts
More informationCorrection of Radar QPE Errors Associated with Low and Partially Observed Brightband Layers
DECEMBER 2013 Q I A N D Z H A N G 1933 Correction of Radar QPE Errors Associated with Low and Partially Observed Brightband Layers YOUCUN QI Cooperative Institute for Mesoscale Meteorological Studies,
More informationHuw W. Lewis *, Dawn L. Harrison and Malcolm Kitchen Met Office, United Kingdom
2.6 LOCAL VERTICAL PROFILE CORRECTIONS USING DATA FROM MULTIPLE SCAN ELEVATIONS Huw W. Lewis *, Dawn L. Harrison and Malcolm Kitchen Met Office, United Kingdom 1. INTRODUCTION The variation of reflectivity
More informationDiagnosing the Intercept Parameter for Exponential Raindrop Size Distribution Based on Video Disdrometer Observations: Model Development
Diagnosing the Intercept Parameter for Exponential Raindrop Size Distribution Based on Video Disdrometer Observations: Model Development Guifu Zhang 1, Ming Xue 1,2, Qing Cao 1 and Daniel Dawson 1,2 1
More informationSpatial Variability in Differences between Multi-sensor and Raingage Precipitation Estimates within the Central United States
Spatial Variability in Differences between Multi-sensor and Raingage Precipitation Estimates within the Central United States Nancy E. Westcott Center for Atmospheric Sciences Illinois State Water Survey
More informationP4.4 THE COMBINATION OF A PASSIVE MICROWAVE BASED SATELLITE RAINFALL ESTIMATION ALGORITHM WITH AN IR BASED ALGORITHM
P4.4 THE COMBINATION OF A PASSIVE MICROWAVE BASED SATELLITE RAINFALL ESTIMATION ALGORITHM WITH AN IR BASED ALGORITHM Robert Joyce 1), John E. Janowiak 2), and Phillip A. Arkin 3, Pingping Xie 2) 1) RS
More informationBasins-Level Heavy Rainfall and Flood Analyses
Basins-Level Heavy Rainfall and Flood Analyses Peng Gao, Greg Carbone, and Junyu Lu Department of Geography, University of South Carolina (gaop@mailbox.sc.edu, carbone@mailbox.sc.edu, jlu@email.sc.edu)
More information*Corresponding author address: Charles Barrere, Weather Decision Technologies, 1818 W Lindsey St, Norman, OK
P13R.11 Hydrometeorological Decision Support System for the Lower Colorado River Authority *Charles A. Barrere, Jr. 1, Michael D. Eilts 1, and Beth Clarke 2 1 Weather Decision Technologies, Inc. Norman,
More informationSAMPLE. SITE SPECIFIC WEATHER ANALYSIS Wind Report. Robinson, Smith & Walsh. John Smith. July 1, 2017 REFERENCE: 1 Maple Street, Houston, TX 77034
SAMPLE SITE SPECIFIC WEATHER ANALYSIS Wind Report PREPARED FOR: Robinson, Smith & Walsh John Smith July 1, 2017 REFERENCE: JACK HIGGINS / 4151559-01 1 Maple Street, Houston, TX 77034 CompuWeather Sample
More informationThe Shape Slope Relation in Observed Gamma Raindrop Size Distributions: Statistical Error or Useful Information?
116 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME The Shape Slope Relation in Observed Gamma Raindrop Size Distributions: Statistical Error or Useful Information? GUIFU ZHANG, J.VIVEKANANDAN, AND
More informationDisdrometric data analysis and related microphysical processes
Author: Garcia Facultat de Física, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain. Advisor: Joan Bech Rustullet Abstract: The present paper consists in the analysis of Rain Drop Size Distribution
More informationUsing Cell-Based VIL Density to Identify Severe-Hail Thunderstorms in the Central Appalachians and Middle Ohio Valley
EASTERN REGION TECHNICAL ATTACHMENT NO. 98-9 OCTOBER, 1998 Using Cell-Based VIL Density to Identify Severe-Hail Thunderstorms in the Central Appalachians and Middle Ohio Valley Nicole M. Belk and Lyle
More informationEvaluation of MPE Radar Estimation Using a High Density Rain Gauge Network within a Hydro-Estimator Pixel and Small SubWatershed
Evaluation of MPE Radar Estimation Using a High Density Rain Gauge Network within a Hydro-Estimator Pixel and Small SubWatershed ALEJANDRA M. ROJAS GONZÁLEZ 1, ERIC W. HARMSEN 2 AND SANDRA CRUZ POL 3 1
More informationTHE USE OF COMPARISON CALIBRATION OF REFLECTIVITY FROM THE TRMM PRECIPITATION RADAR AND GROUND-BASED OPERATIONAL RADARS
THE USE OF COMPARISON CALIBRATION OF REFLECTIVITY FROM THE TRMM PRECIPITATION RADAR AND GROUND-BASED OPERATIONAL RADARS Lingzhi-Zhong Rongfang-Yang Yixin-Wen Ruiyi-Li Qin-Zhou Yang-Hong Chinese Academy
More informationAnalysis of radar and gauge rainfall during the warm season in Oklahoma
Analysis of radar and gauge rainfall during the warm season in Oklahoma Bin Wang 1, Jian Zhang 2, Wenwu Xia 2, Kenneth Howard 3, and Xiaoyong Xu 2 1 Wuhan Institute of Heavy Rain, China Meteorological
More informationAn empirical method to improve rainfall estimation of dual polarization radar using ground measurements
An empirical method to improve rainfall estimation of dual polarization radar using ground measurements 5 Jungsoo Yoon 1, Mi-Kyung Suk 1, Kyung-Yeub Nam 1, Jeong-Seok Ko 1, Hae-Lim Kim 1, Jong-Sook Park
More informationDetailed Storm Rainfall Analysis for Hurricane Ivan Flooding in Georgia Using the Storm Precipitation Analysis System (SPAS) and NEXRAD Weather Radar
Detailed Storm Rainfall Analysis for Hurricane Ivan Flooding in Georgia Using the Storm Precipitation Analysis System (SPAS) and NEXRAD Weather Radar Ed Tomlinson, PhD and Bill Kappel Applied Weather Associates
More informationThe Use of TRMM Precipitation Radar Observations in Determining Ground Radar Calibration Biases
616 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 18 The Use of TRMM Precipitation Radar Observations in Determining Ground Radar Calibration Biases EMMANOUIL N. ANAGNOSTOU, CARLOS A. MORALES, AND
More informationP2.20 Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals
P2.20 Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals H.- S. Park 1, A. Ryzhkov 2, H. Reeves 2, and T. Schuur 2 1 Department
More informationUWM Field Station meteorological data
University of Wisconsin Milwaukee UWM Digital Commons Field Station Bulletins UWM Field Station Spring 992 UWM Field Station meteorological data James W. Popp University of Wisconsin - Milwaukee Follow
More informationImpact of seasonal variation of raindrop size distribution (DSD) on DSD retrieval methods based on polarimetric radar measurements
Impact of seasonal variation of raindrop size distribution (DSD) on DSD retrieval methods based on polarimetric radar measurements K.Amar Jyothi 1, T.Narayana Rao 2, S.Vijaya Bhaskara Rao 3, S.Rajendran
More informationFinal Report. COMET Partner's Project. University of Texas at San Antonio
Final Report COMET Partner's Project University: Name of University Researcher Preparing Report: University of Texas at San Antonio Dr. Hongjie Xie National Weather Service Office: Name of National Weather
More informationRemote Sensing of Precipitation
Lecture Notes Prepared by Prof. J. Francis Spring 2003 Remote Sensing of Precipitation Primary reference: Chapter 9 of KVH I. Motivation -- why do we need to measure precipitation with remote sensing instruments?
More informationP4.8 PERFORMANCE OF A NEW VELOCITY DEALIASING ALGORITHM FOR THE WSR-88D. Arthur Witt* and Rodger A. Brown
P4.8 PERFORMANCE OF A NEW VELOCITY DEALIASING ALGORITHM FOR THE WSR-88D Arthur Witt* and Rodger A. Brown NOAA/National Severe Storms Laboratory, Norman, Oklahoma Zhongqi Jing NOAA/National Weather Service
More informationCorrections to Scatterometer Wind Vectors For Precipitation Effects: Using High Resolution NEXRAD and AMSR With Intercomparisons
Corrections to Scatterometer Wind Vectors For Precipitation Effects: Using High Resolution NEXRAD and AMSR With Intercomparisons David E. Weissman Hofstra University Hempstead, New York 11549 Svetla Hristova-Veleva
More informationRainfall Estimation with a Polarimetric Prototype of WSR-88D
502 JOURNAL OF APPLIED METEOROLOGY VOLUME 44 Rainfall Estimation with a Polarimetric Prototype of WSR-88D ALEXANDER V. RYZHKOV, SCOTT E. GIANGRANDE, AND TERRY J. SCHUUR Cooperative Institute for Mesoscale
More informationAn Optimal Area Approach to Intercomparing Polarimetric Radar Rain-Rate Algorithms with Gauge Data
VOLUME 15 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY JUNE 1998 An Optimal Area Approach to Intercomparing Polarimetric Radar Rain-Rate Algorithms with Gauge Data S. BOLEN Rome Laboratory, USAF/Rome
More informationresults, azimuthal over-sampling and rapid update Y. Umemoto 1, 2, T. Lei 3, T. -Y. Yu 1, 2 and M. Xue 3, 4
P9.5 Examining the Impact of Spatial and Temporal Resolutions of Phased-Array Radar on EnKF Analysis of Convective Storms Using OSSEs - Modeling Observation Errors Y. Umemoto, 2, T. Lei 3, T. -Y. Yu, 2
More informationImproving QPE for Tropical Systems with Environmental Moisture Fields and Vertical Profiles of Reflectivity
Improving QPE for Tropical Systems with Environmental Moisture Fields and Vertical Profiles of Reflectivity Heather Moser 12 *, Kenneth Howard 2, Jian Zhang 2, and Steven Vasiloff 2 1 Cooperative Institute
More informationX-band Polarimetric Radar Rainfall Measurements in Keys Area Microphysics Project
JANUARY 2006 ANAGNOSTOU ET AL. 187 X-band Polarimetric Radar Rainfall Measurements in Keys Area Microphysics Project EMMANOUIL N. ANAGNOSTOU Department of Civil and Environmental Engineering, University
More informationNOTES AND CORRESPONDENCE. A Quantitative Estimate of the Effect of Aliasing in Climatological Time Series
3987 NOTES AND CORRESPONDENCE A Quantitative Estimate of the Effect of Aliasing in Climatological Time Series ROLAND A. MADDEN National Center for Atmospheric Research,* Boulder, Colorado RICHARD H. JONES
More informationComparison of polarimetric radar signatures in hailstorms simultaneously observed by C-band and S-band radars.
Comparison of polarimetric radar signatures in hailstorms simultaneously observed by C-band and S-band radars. R. Kaltenboeck 1 and A. Ryzhkov 2 1 Austrocontrol - Aviation Weather Service, Vienna and Institute
More informationBY REAL-TIME ClassZR. Jeong-Hee Kim 1, Dong-In Lee* 2, Min Jang 2, Kil-Jong Seo 2, Geun-Ok Lee 2 and Kyung-Eak Kim 3 1.
P2.6 IMPROVEMENT OF ACCURACY OF RADAR RAINFALL RATE BY REAL-TIME ClassZR Jeong-Hee Kim 1, Dong-In Lee* 2, Min Jang 2, Kil-Jong Seo 2, Geun-Ok Lee 2 and Kyung-Eak Kim 3 1 Korea Meteorological Administration,
More informationSAMPLE. SITE SPECIFIC WEATHER ANALYSIS Slip and Fall on Snow/Ice. Robinson, Smith & Walsh. John Smith. July 1, 2017 REFERENCE:
SAMPLE SITE SPECIFIC WEATHER ANALYSIS Slip and Fall on Snow/Ice PREPARED FOR: Robinson, Smith & Walsh John Smith July 1, 2017 REFERENCE: MARIE DAVIDSON / 202301 1 Jackson Drive, Hicksville, NY 11801 CompuWeather
More informationP1.10 Synchronization of Multiple Radar Observations in 3-D Radar Mosaic
Submitted for the 12 th Conf. on Aviation, Range, and Aerospace Meteor. 29 Jan. 2 Feb. 2006. Atlanta, GA. P1.10 Synchronization of Multiple Radar Observations in 3-D Radar Mosaic Hongping Yang 1, Jian
More informationP1.6 Simulation of the impact of new aircraft and satellite-based ocean surface wind measurements on H*Wind analyses
P1.6 Simulation of the impact of new aircraft and satellite-based ocean surface wind measurements on H*Wind analyses Timothy L. Miller 1, R. Atlas 2, P. G. Black 3, J. L. Case 4, S. S. Chen 5, R. E. Hood
More informationDancers from Dora Stratou welcome us to Greece Ionian Sea Rainfall Experiment
Dancers from Dora Stratou welcome us to Greece Ionian Sea Rainfall Experiment Southwest of Pylos, Messinia the Ionian Sea is over 3 km deep within 20 km of shore One of the biggest impacts of climate change
More informationModule 11: Meteorology Topic 5 Content: Weather Maps Notes
Introduction A variety of weather maps are produced by the National Weather Service and National Oceanographic Atmospheric Administration. These maps are used to help meteorologists accurately predict
More information3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL
3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Q. Zhao 1*, J. Cook 1, Q. Xu 2, and P. Harasti 3 1 Naval Research Laboratory, Monterey,
More informationChapter 12: Meteorology
Chapter 12: Meteorology Section 1: The Causes of Weather 1. Compare and contrast weather and climate. 2. Analyze how imbalances in the heating of Earth s surface create weather. 3. Describe how and where
More informationCorrelation between lightning types
GEOPHYSICAL RESEARCH LETTERS, VOL. 34,, doi:10.1029/2007gl029476, 2007 Correlation between lightning types J. L. Lapp 1 and J. R. Saylor 1 Received 25 January 2007; revised 21 February 2007; accepted 20
More informationAssessment of QPE results from 4 kw X-band Local Area Weather Radar (LAWR) evaluated with S-band radar data
Assessment of QPE results from 4 kw X-band Local Area Weather Radar (LAWR) evaluated with S-band radar data Lisbeth Pedersen 1+3, Isztar. Zawadzki 2, Niels Einar Jensen 1 and Henrik Madsen 3, (1) DHI,
More informationIMPROVED QUANTITATIVE ESTIMATION OF RAINFALL BY RADAR
IMPROVED QUANTITATIVE ESTIMATION OF RAINFALL BY RADAR by Md Rashedul Islam A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba in partial fulfilment of the requirements
More informationAN ANALYSIS OF A SHALLOW COLD FRONT AND WAVE INTERACTIONS FROM THE PLOWS FIELD CAMPAIGN
AN ANALYSIS OF A SHALLOW COLD FRONT AND WAVE INTERACTIONS FROM THE PLOWS FIELD CAMPAIGN P.105 Carter Hulsey and Kevin Knupp Severe Weather Institute and Radar & Lightning Laboratories, University of Alabama
More informationDiagnosing the Intercept Parameter for Exponential Raindrop Size Distribution Based on Video Disdrometer Observations: Model Development
NOVEMBER 2008 Z H A N G E T A L. 2983 Diagnosing the Intercept Parameter for Exponential Raindrop Size Distribution Based on Video Disdrometer Observations: Model Development GUIFU ZHANG* School of Meteorology,
More informationPreliminary result of hail detection using an operational S-band polarimetric radar in Korea
Preliminary result of hail detection using an operational S-band polarimetric radar in Korea Mi-Young Kang 1, Dong-In Lee 1,2, Cheol-Hwan You 2, and Sol-Ip Heo 3 1 Department of Environmental Atmospheric
More information13B.4 CPOL RADAR-DERIVED DSD STATISTICS OF STRATIFORM AND CONVECTIVE RAIN FOR TWO REGIMES IN DARWIN, AUSTRALIA
13B.4 CPOL RADAR-DERIVED DSD STATISTICS OF STRATIFORM AND CONVECTIVE RAIN FOR TWO REGIMES IN DARWIN, AUSTRALIA M. Thurai 1*, V. N. Bringi 1, and P. T. May 2 1 Colorado State University, Fort Collins, Colorado,
More informationAn Examination of Radar and Rain Gauge Derived Mean Areal Precipitation over Georgia Watersheds
FEBRUARY 2001 STELLMAN ET AL. 133 An Examination of Radar and Rain Gauge Derived Mean Areal Precipitation over Georgia Watersheds KEITH M. STELLMAN* AND HENRY E. FUELBERG Department of Meteorology, The
More informationStratiform and Convective Classification of Rainfall Using SSM/I 85-GHz Brightness Temperature Observations
570 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 14 Stratiform and Convective Classification of Rainfall Using SSM/I 85-GHz Brightness Temperature Observations EMMANOUIL N. ANAGNOSTOU Department
More informationFREEZING DRIZZLE DETECTION WITH WSR-88D RADARS
7A.2 FREEZING DRIZZLE DETECTION WITH WSR-88D RADARS Kyoko Ikeda, Roy M. Rasmussen, and Edward A. Brandes National Center for Atmospheric Research, Boulder, Colorado 1. Introduction Freezing drizzle represents
More information