scholarly journals Where the Least Rainfall Occurs in the Sahara Desert, the TRMM Radar Reveals a Different Pattern of Rainfall Each Season

2014 ◽  
Vol 27 (18) ◽  
pp. 6919-6939 ◽  
Author(s):  
Owen A. Kelley
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Seasonal Patterns ◽  
Sahara Desert ◽  
Precipitation Radar ◽  
Light Rain ◽  
Rain Gauges ◽  
The North ◽  
Imaging Sensor ◽  
Summer Rain ◽  
Rainfall Climatology

Abstract Some previous studies were unable to detect seasonal organization to the rainfall in the Sahara Desert, while others reported seasonal patterns only in the less-arid periphery of the Sahara. In contrast, the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) satellite detects four rainy seasons in the part of the Sahara where the TRMM radar saw the least rainfall during a 15-yr period (1998–2012). According to the TRMM radar, approximately 20°–27°N, 22°–32°E is the portion of the Sahara that has the lowest average annual rain accumulation (1–5 mm yr−1). Winter (January and February) has light rain throughout this region but more rain to the north over the Mediterranean Sea. Spring (April and May) has heavier rain and has lightning observed by the TRMM Lightning Imaging Sensor (LIS). Summer rain and lightning (July and August) occur primarily south of 23°N. At a maximum over the Red Sea, autumn rain and lightning (October and November) can be heavy in the northeastern portion of the study area, but these storms are unreliable: that is, the TRMM radar detects such storms in only 6 of the 15 years. These four rainy seasons are each separated by a comparatively drier month in the monthly rainfall climatology. The few rain gauges in this arid region broadly agree with the TRMM radar on the seasonal organization of rainfall. This seasonality is reason to reevaluate the idea that Saharan rainfall is highly irregular and unpredictable.

scholarly journals Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview

2019 ◽  
Vol 11 (1) ◽  
pp. 80 ◽  
Author(s):  
Nan Li ◽  
Zhenhui Wang ◽  
Xi Chen ◽  
Geoffrey Austin
Keyword(s):  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Future Prospects ◽  
Precipitation Radar ◽  
Tropical Regions ◽  
Existing Problems ◽  
Rain Gauges ◽  
Trmm Pr ◽  
Continuous Rainfall

The Precipitation Radar (PR), the first space-borne precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, could observe three-dimensional precipitation in global tropical regions and acquire continuous rainfall information with moderate temporal and high spatial resolutions. TRMM PR had carried out 17 years of observations and ended collecting data in April, 2015. So far, comprehensive and abundant research results related to the application of PR data have been analyzed in the current literature, but overall precipitation features are not yet identified, a gap that this review intends to fill. Studies on comparisons with ground-based radars and rain gauges are first introduced to summarize the reliability of PR observations or estimates. Then, this paper focuses on general precipitation features abstracted from about 130 studies, from 2000 to 2018, regarding lightning analysis, latent heat retrieval, and rainfall observation by PR data. Finally, we describe the existing problems and limitations as well as the future prospects of the space-borne precipitation radar data.

scholarly journals Study of Rainfall from TRMM Microwave Imager Observation over India

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Anoop Kumar Mishra ◽  
Rajesh Kumar
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Training Data ◽  
Precipitation Radar ◽  
Data Set ◽  
Rain Gauges ◽  
Indian Land ◽  
Microwave Imager ◽  
Scattering Index ◽  
Trmm Microwave Imager ◽  
Rain Rates

This paper presents a technique to estimate precipitation over Indian land (6–36°N, 65–99°E) at 0.25∘×0.25∘ spatial grid using tropical rainfall measuring mission (TRMM) microwave imager (TMI) observations. It adopts the methodology recently developed by Mishra (2012) to monitor the rainfall over the land portion. Regional scattering index (SI) developed for Indian region and polarization corrected temperature (PCT) have been utilized in this study. These proxy rain variables (i.e., PCT and SI) are matched with rainfall from precipitation radar (PR) to relate rain rate with PCT, SI, and their combination. Retrieval techniques have been developed using nonlinear relationship between rain and proxy variables. The results have been compared with the observations (independent of training data set) from PR. Results have also been validated with the observations from automatic weather station (AWS) rain gauges. It is observed from the validation results that nonlinear algorithm using single variable SI underestimates the low rainfall rates (below 20 mm/h) but overestimates the high rain rates (above 20 mm/h). On the other hand, algorithm using PCT overestimates the high rain rates (above 25 mm/h). Validation results with rain gauges show a CC of 0.68 and RMSE of 4.76 mm when both SI and PCT are used.

scholarly journals A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

2021 ◽  
Vol 13 (6) ◽  
pp. 1208
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python ◽  
Jian Peng
Keyword(s):  
Comprehensive Evaluation ◽  
Probability Of Detection ◽  
Light Rain ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Major River ◽  
Low Levels ◽  
Rain Events ◽  
Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

scholarly journals Spatiotemporal Variability of Lightning Flash Distribution over Sri Lanka

2019 ◽  
Vol 82 ◽  
pp. 1-13
Author(s):  
U.G.Dilaj Maduranga ◽  
Mahesh Edirisinghe ◽  
L. Vimukthi Gamage
Keyword(s):  
Sri Lanka ◽  
Monsoon Season ◽  
Tropical Rainfall Measuring Mission ◽  
Spatiotemporal Variability ◽  
Spatial And Temporal Variation ◽  
Lightning Flash ◽  
Northeast Monsoon ◽  
Time Period ◽  
Total Lightning ◽  
Imaging Sensor

The variation of the lightning activities over Sri Lanka and surrounded costal belt (5.750N-10.000N and 79.50E-89.000E) is studied using lightning flash data of Lightning Imaging Sensor (LIS) which was launched in November 1997 for NASA’s Tropical Rainfall Measuring Mission (TRMM). The LIS data for the period of 1998 to 2014 are considered for this study. The spatial and temporal variation of lightning activities is investigated and respective results are presented. The diurnal variation over the studied area presents that maximum and minimum flash count recorded at 1530-1630 Local Time (10-11UTC) and 0530-0630LT (00-01UTC) respectively. Maximum lightning activities over the observed area have occurred after the 1330LT (08UTC) in every year during the considered time period. The seasonal variation of the lightning activities shows that the maximum lightning activities happened in First inter monsoon season (March to April) with 30.90% total lightning flashes and minimum lightning activities recorded in Northeast monsoon season (December to February) with 8.51% of total lightning flashes. Maximum flash density of 14.37fl km-2year-1 was observed at 6.980N/80.160E in First inter monsoon season. These seasonal lighting activities are agree with seasonal convective activities and temperature variation base on propagation of Intra-Tropical Convection Zone over the studied particular area. Mean monthly flash count presents a maximum in the month of April with 29.12% of lightning flashes. Variation pattern of number of lightning activities in month of April shows a tiny increment during the time period of 1998 to 2014. Maximum annual flash density of 28.09fl km-2yr-1 was observed at 6.980N/80.170E. The latitudinal variation of the lightning flash density is depicted that extreme lightning activities have happened at the southern part of the county and results show that there is a noticeable lack of lightning activities over the surrounded costal belt relatively landmass.

scholarly journals Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1624 ◽  
Author(s):  
Akbari ◽  
Haghighi ◽  
Aghayi ◽  
Javadian ◽  
Tajrishy ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Curve Number ◽  
Rain Gauge ◽  
Runoff Coefficient ◽  
Lake Urmia ◽  
Direct Runoff ◽  
Rain Gauges ◽  
Elevation Model ◽  
High Elevations ◽  
Service Curve

Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.

XVI.—Mirage on the Queensferry Road

1919 ◽  
Vol 38 ◽  
pp. 166-168 ◽  
Author(s):  
Alexander G. Ramage
Keyword(s):  
Sahara Desert ◽  
Clear Water ◽  
The Road ◽  
The North ◽  
White Horse ◽  
The World ◽  
As If ◽  
North End

Most of us who have thought at all of mirage have thought of it as a phenomenon belonging essentially to distant parts of the world, such as the great Sahara Desert. Few of us would not be surprised to find it almost a daily spectacle on a familiar road so near our city.You may imagine my surprise when, in the early days of April of this year, while walking westward along the Queensferry Road, and when opposite the quarry at the north end of Corstorphine Hill near the point at which the Corstorphine Hill road joins the Queensferry Road, I saw on the surface of the road, at a distance of about one and a half the spacing of the telegraph poles (they are about 50 paces apart), what appeared to be pools of clear water reflecting the green grass and foliage very clearly, and further down the road other pools. As I watched, a white horse with a rider went along, and as it passed beyond the “pools” of the mirage water (the road being perfectly dry) it was reflected, with the effect that the horse appeared to be about twice its height, as if on stilts.

scholarly journals Preliminary Observations from the China Fengyun-4A Lightning Mapping Imager and Its Optical Radiation Characteristics

2020 ◽  
Vol 12 (16) ◽  
pp. 2622 ◽  
Author(s):  
Wen Hui ◽  
Wenjuan Zhang ◽  
Weitao Lyu ◽  
Pengfei Li
Keyword(s):  
Data Processing ◽  
Optical Radiation ◽  
Monsoon Season ◽  
Tropical Rainfall Measuring Mission ◽  
Radiation Characteristics ◽  
Southeastern China ◽  
Northeastern India ◽  
Satellite Platform ◽  
Variation Characteristics ◽  
Imaging Sensor

The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first satellite-borne lightning imager developed in China, which can detect lightning over China and its neighboring regions based on a geostationary satellite platform. In this study, the spatial distribution and temporal variation characteristics of lightning activity over China and its neighboring regions were analyzed in detail based on 2018 LMI observations. The observation characteristics of the LMI were revealed through a comparison with the Tropical Rainfall Measuring Mission (TRMM)-Lightning Imaging Sensor (LIS) and World Wide Lightning Location Network (WWLLN) observations. Moreover, the optical radiation characteristics of lightning signals detected by the LMI were examined. Factors that may affect LMI detection were discussed by analyzing the differences in optical radiation characteristics between LMI and LIS flashes. The results are as follows. Spatially, the flash density distribution pattern detected by the LMI was similar to those detected by the LIS and WWLLN. High-flash density regions were mainly concentrated over Southeastern China and Northeastern India. Temporally, LMI flashes exhibited notable seasonal and diurnal variation characteristics. The LMI detected a concentrated lightning outbreak over Northeastern India in the premonsoon season and over Southeastern China in the monsoon season, which was consistent with LIS and WWLLN observations. LMI-observed diurnal peak flash rates occurred in the afternoon over most of the regions. There was a “stepwise” decrease in the LMI-observed optical radiance, footprint size, duration, and number of groups per flash, from the ocean to the coastal regions to the inland regions. LMI flashes exhibited higher optical radiance but lasted for shorter durations than LIS flashes. LMI observations are not only related to instrument performance but are also closely linked to onboard and ground data processing. In future, targeted improvements can be made to the data processing algorithm for the LMI to further enhance its detection capability.

scholarly journals Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 705
Author(s):  
Chung-Chieh Wang ◽  
Sahana Paul ◽  
Dong-In Lee
Keyword(s):  
Object Oriented ◽  
Tropical Rainfall Measuring Mission ◽  
Weather Research And Forecasting ◽  
Total Rainfall ◽  
Mesoscale Models ◽  
Subjective Analysis ◽  
Verification Method ◽  
Rain Gauges ◽  
Object Based ◽  
Central Weather Bureau

In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

Changes in TRMM Rainfall due to the Orbit Boost Estimated from Buoy Rain Gauge Data

2007 ◽  
Vol 24 (9) ◽  
pp. 1598-1607 ◽  
Author(s):  
Jeremy D. DeMoss ◽  
Kenneth P. Bowman
Keyword(s):  
Sampling Error ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Rate Dependent ◽  
Wide Range ◽  
The Mean ◽  
Microwave Imager ◽  
Trmm Microwave Imager

Abstract During the first three-and-a-half years of the Tropical Rainfall Measuring Mission (TRMM), the TRMM satellite operated at a nominal altitude of 350 km. To reduce drag, save maneuvering fuel, and prolong the mission lifetime, the orbit was boosted to 403 km in August 2001. The change in orbit altitude produced small changes in a wide range of observing parameters, including field-of-view size and viewing angles. Due to natural variability in rainfall and sampling error, it is not possible to evaluate possible changes in rainfall estimates from the satellite data alone. Changes in TRMM Microwave Imager (TMI) and the precipitation radar (PR) precipitation observations due to the orbit boost are estimated by comparing them with surface rain gauges on ocean buoys operated by the NOAA/Pacific Marine Environment Laboratory (PMEL). For each rain gauge, the bias between the satellite and the gauge for pre- and postboost time periods is computed. For the TMI, the satellite is biased ∼12% low relative to the gauges during the preboost period and ∼1% low during the postboost period. The mean change in bias relative to the gauges is approximately 0.4 mm day−1. The change in TMI bias is rain-rate-dependent, with larger changes in areas with higher mean precipitation rates. The PR is biased significantly low relative to the gauges during both boost periods, but the change in bias from the pre- to postboost period is not statistically significant.

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