scholarly journals Vertical Variation of Z-R Relationship at Hallasan Mountain during Typhoon Nakri in 2014

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Chulsang Yoo ◽  
Jung Mo Ku
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Rainfall Event ◽  
Jeju Island ◽  
Vertical Variation ◽  
Rain Gauge Data ◽  
Orographic Effect ◽  
Gauge Data ◽  
Reflectivity Data ◽  
Average Rain

Hallasan Mountain is located at the center of Jeju Island, Korea. Even though the height of the mountain is just 1,950 m, the orographic effect is strong enough to cause heavy rainfall. In this study, a rainfall event, due to Typhoon Nakri in 2014, observed in Jeju Island was analyzed fully using the radar and rain gauge data. First, the Z-R relationship Z=ARb was derived for every 250 m interval from the sea level to the mountain top. The resulting Z-R relationships showed that the exponent b could be assumed as constant but that the parameter A showed a significant decreasing trend up to an altitude around 1,000 m before it increased again. The orographic effect was found to be most significant at this altitude of 1,000 m. Second, the derived Z-R relationships were applied to the corresponding altitude radar reflectivity data to generate the rain rate field over Jeju Island. This rain rate field was then used to derive the areal-average rain rate data. These data were found to be very similar to the rain gauge estimates but were significantly different from those derived from the application of the Marshall-Palmer equation to the 1.5 km CAPPI data, which is the data type that is generally used by the Korea Meteorological Administration (KMA).

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Rain-Rate Estimation in the Presence of Hail Using S-Band Specific Differential Phase and Other Radar Parameters

1995 ◽  
Vol 34 (2) ◽  
pp. 404-410 ◽  
Author(s):  
K. Aydin ◽  
V. N. Bringi ◽  
L. Liu
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Heavy Rainfall Event ◽  
Rain Gauge Data ◽  
Differential Phase ◽  
X Band ◽  
Gauge Data ◽  
Radar Measurements ◽  
Multiparameter Radar ◽  
Good Agreement

Abstract Multiparameter radar measurements were made during a heavy rainfall event accompanied by hail in Colorado. Rainfall rates R and accumulation Σ for this event were estimated using S-band specific differential phase KDP, reflectivity factor ZH, and X-band specific attenuation AH3. These estimates were compared with measurements from a ground-based rain gauge. Both R–KDP and R–AH3 relations were in good agreement with the rain gauge data, that is, less than 10% difference in the rainfall accumulations. The R–Z relation produced similar results only when ZH was truncated at 55 dBZ. This study demonstrates the potential of KDP for estimating rainfall rates in severe storms that may have rain-hail mixtures.

scholarly journals Combination of Radar and Rain Gauge Information to Map the Snowy Region in Jeju Island, Korea: A Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jung Mo Ku ◽  
Chulsang Yoo
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Coastal Areas ◽  
Radar Reflectivity ◽  
Jeju Island ◽  
Rain Gauges ◽  
Rate Information ◽  
Fixed Threshold ◽  
Reflectivity Data

Hallasan Mountain is located at the center of Jeju Island, Korea. Even though Hallasan Mountain has a height of just 1,950 m, the temperature during the winter decreases below −20 degrees Celsius. On the contrary, the temperature on the coastal areas remains just above freezing. Therefore, large snowfalls in the mountain and rainfall in the coastal areas are very common in Jeju Island. Most of the rain gauges are available around highly populated coastal areas, and snow measurements are available at just four locations on the coastal areas. Therefore, it is practically impossible to distinguish the rainfall and snowfall in Jeju Island. Fortunately, two radars (Seongsan and Gosan radars) operate on Jeju Island, which fully covers Hallasan Mountain. This study proposes a method of using both the radar and rain gauge information to map the snowy region in Jeju Island, including Hallasan Mountain. As a first step, this study analyzed the Z-R and Z-S relationships to derive a fixed threshold of radar reflectivity to separate snowfall from rainfall, and, in the second step, this study additionally considered the observed rain rate information to implement the problem of using the fixed threshold. This proposed method was applied to radar reflectivity data collected during November 1, 2014, to April 30, 2015, and the results indicate that the method considering both the radar and rain gauge information was satisfactory. This method also showed good performance, especially when the rain rate was very low.

scholarly journals Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

2021 ◽  
Vol 13 (11) ◽  
pp. 2217
Author(s):  
Wenyue Wang ◽  
Klemens Hocke ◽  
Christian Mätzler
Keyword(s):  
Rain Rate ◽  
Radiative Transfer Equation ◽  
Microwave Radiometer ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Retrieval Method ◽  
Rain Gauge Data ◽  
Rate Estimation ◽  
Gauge Data ◽  
Microwave Radiometers

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.

scholarly journals Rain rate distributions for China from hourly rain gauge data

Radio Science ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553-564 ◽  
Author(s):  
Li Li ◽  
Yuan-jing Zhu ◽  
Bolin Zhao
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data

Accuracy of Quantitative Precipitation Estimation Using Operational Weather Radars: A Case Study of Heavy Rainfall on 9–10 September 2015 in the East Kanto Region, Japan

2016 ◽  
Vol 11 (5) ◽  
pp. 1003-1016 ◽  
Author(s):  
Shakti P. C. ◽  
◽  
Ryohei Misumi ◽  
Tsuyoshi Nakatani ◽  
Koyuru Iwanami ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Rainfall Event ◽  
Rain Gauge Data ◽  
Frequency Distributions ◽  
Radar Network ◽  
Radar Networks ◽  
Gauge Data ◽  
Kanto Region

On 9–10 September 2015, the East Kanto region of Japan experienced a period of record-breaking heavy rainfall that caused a number of fatalities and serious property damage. The maximum 24-hr rainfall total (0600 UTC 9 September 2015 to 0600 UTC 10 September 2015), about 500 mm, was recorded over Tochigi Prefecture. Spatial and temporal variations in the meteorological and hydrological characteristics of this rainfall event were analyzed using data from the Japan Meteorological Agency’s (JMA) C-band radar network and data from the X-band polarimetric radar network (XRAIN). The rain gauge data available from the Kanto region has a temporal resolution of 10 min. The spatial and temporal resolutions of the JMA C-band radar data are 1000 m and 5 min, respectively, whereas the XRAIN radar has spatial and temporal resolutions of 250 m and 1 min, respectively. Data from the two radar networks were compared, both with each other and with data from various rain gauge networks to validate their accuracy. The 24-hr total rainfall data from both radar networks showed frequency distributions similar to those showed by the rain gauge data. However, the JMA and XRAIN data showed different distributions for the higher rainfall intensity thresholds. There was no relationship evident between rainfall and elevation in either of the radar datasets recorded during this event. The spatial distribution of rainfall over the study area derived from XRAIN showed clear variations, whereas the JMA radar did not. This is most probably related to the coarser spatial and temporal resolutions of the JMA observations. Based on a comparison of data from the rain gauge and radar networks, the XRAIN data more accurately reflected the rain gauge stations than did the JMA data. From a hydrological perspective, the Kinugawa watershed is unique in terms of its topography. The upper part of the watershed is wide and mountainous, whereas the rest is narrow and elongate north–south. The rain echo moved from south to north over the catchment, and the highest 24-hr accumulated rainfall totals were recorded mostly in the upper (northern) part of the Kinugawa watershed, whereas there was less rainfall in the lower (southern) part. This pattern suggests a high probability of serious flooding along the Kinugawa River in the days following such a rainfall event if the heaviest rainfall moves northwards over the watershed.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

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