scholarly journals Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

2020 ◽  
Vol 21 (7) ◽  
pp. 1549-1569 ◽  
Author(s):  
Pravat Jena ◽  
Sourabh Garg ◽  
Sarita Azad
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Event Analysis ◽  
Gridded Data ◽  
Precipitation Estimates ◽  
Indian Meteorological Department ◽  
Northwest Himalayas ◽  
Rain Gauge Network

AbstractThe presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° × 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2, and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4% to 53.9% over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6%–48.4%) and IMERG (4.9%–17.8%). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8%–74.4%, followed by PERSIANN-CDR (34.4%–69.11%) and IMERG (15.3%–39.0%). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5%–78.6%.

scholarly journals Assessment of small-scale variability of rainfall and multisatellite precipitation estimates using a meso-rain gauge network measurements from southern peninsular India

2015 ◽  
Vol 12 (10) ◽  
pp. 10389-10429
Author(s):  
K. Sunilkumar ◽  
T. Narayana Rao ◽  
S. Satheeshkumar
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Diurnal Cycle ◽  
Rain Gauge ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Northeast Monsoon ◽  
Peninsular India ◽  
Precipitation Estimates ◽  
Rain Gauge Network

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain storms (both in space and time) over a complex hilly terrain in southeast peninsular India. Three years of high-resolution gauge measurements are used to evaluate 3 hourly rainfall and sub-daily variations of four widely used multisatellite precipitation estimates (MPEs). The network consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of ~ 10 km. Morphological features of rainfall in two principal monsoon seasons (southwest monsoon: SWM and northeast monsoon: NEM) show marked seasonal differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale systems (in wet spells), whereas the contribution from small-scale systems is considerable in SWM. Rain storms with longer duration and copious rainfall are seen mostly in the western quadrants in SWM and northern quadrants in NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits marked spatiotemporal variability with strong diurnal cycle at all the stations (except for 1) during the SWM and insignificant diurnal cycle at many stations during the NEM. On average, the diurnal amplitudes are a factor 2 larger in SWM than in NEM. The 24 h harmonic explains about 70 % of total variance in SWM and only ~ 30 % in NEM. The late night-mid night peak (20:00–02:00 LT) observed during the SWM is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both the monsoons. The 1 h resolution data shows the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ~30 km in both monsoons. Evaluation of high-resolution rainfall estimates from various MPEs against the gauge rainfall indicates that all MPEs underestimate the weak and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions, however, considerable improvement is observed at 24 h resolution. Among different MPEs, Climate Prediction Centre morphing technique (CMORPH) performs better at 3 hourly resolution in both monsoons. The performance of TRMM multisatellite precipitation analysis (TMPA) is much better at daily resolution than at 3 hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in NEM.

scholarly journals Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

2016 ◽  
Vol 20 (5) ◽  
pp. 1719-1735 ◽  
Author(s):  
K. Sunilkumar ◽  
T. Narayana Rao ◽  
S. Satheeshkumar
Keyword(s):  
High Resolution ◽  
Diurnal Cycle ◽  
Rain Gauge ◽  
Small Scale ◽  
Study Region ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Rain Events ◽  
Rain Gauge Network ◽  
Southeast India

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and sub-daily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km  ×  50 km with an intergauge distance of 6–12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons. Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in the NEM.

scholarly journals RAINBOW: An Operational Oriented Combined IR-Algorithm

2020 ◽  
Vol 12 (15) ◽  
pp. 2444
Author(s):  
Leo Pio D’Adderio ◽  
Silvia Puca ◽  
Gianfranco Vulpiani ◽  
Marco Petracca ◽  
Paolo Sanò ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Probability Of Detection ◽  
Precipitation Rate ◽  
Precipitation Pattern ◽  
European Organization ◽  
Surface Precipitation ◽  
Radar Network ◽  
Infrared Imager ◽  
Precipitation Estimates ◽  
Rain Gauge Network

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.

scholarly journals Improving satellite precipitation estimates over complex terrain through the use of high-resolution numerical weather predictions

2018 ◽  
Author(s):  
Νικόλαος Μπαρτσώτας
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Satellite Precipitation ◽  
Numerical Weather ◽  
Precipitation Estimates

Ο υετός αποτελεί θεμελιώδη παράμετρο για ένα ευρύτατο φάσμα ανθρώπινων δραστηριοτήτων. Τόσο η έλλειψη όσο και οι υπερβολικές του ποσότητες προκαλούν σημαντικές συνέπειες και απειλούν ανθρώπινες ζωές και υποδομές. Η αβεβαιότητα που εξακολουθεί να υπάρχει στην πρόγνωση και επισκόπησή του, έχει σημαντικότατες προεκτάσεις στην γεωργία, τις μεταφορές, την αξιοποίηση υδάτινων πόρων καθώς και την παραγωγή ενέργειας από ανανεώσιμες πηγές. Σε ακραίες εκδοχές φαινομένων υετού, όπως οι πολύ ισχυρές καταιγίδες που συνοδεύονται από ηλεκτρικά φαινόμενα, η αβεβαιότητα αυτή καθίσταται ισχυρότερη. Αυτού του είδους οι καταιγίδες αναπτύσσονται σε πολύ μικρές χωρικές και χρονικές κλίμακες, χαρακτηριστικό το οποίο ανάγει την πρόγνωσή τους σε ιδιαίτερα απαιτητική διαδικασία.Η απαραίτητη πληροφορία είναι επί του παρόντος αδύνατον να προκύψει από μία και μόνο πηγή μέτρησης ή έμμεσης εκτίμησης του υετού, καθώς έκαστη συνοδεύεται από συγκεκριμένους περιορισμούς. Καθίστανται έτσι επιτακτική η ανάγκη προς μια συνδυαστική προσέγγιση. Η συγκεκριμένη διδακτορική διατριβή συνεισφέρει στη δημιουργία καλύτερων εκτιμήσεων υετού πάνω από περιοχές έντονου αναγλύφου, συνδυάζοντας αποτελεσματικά τα επιμέρους θετικά των διαθέσιμων πηγών πληροφορίας. Μετρήσεις από όργανα τηλεπισκόπησης (μετεωρολογικά ραντάρ και δορυφόροι), παρατηρήσεις από δίκτυα βροχομέτρων και ένα πλήθος αριθμητικών μοντέλων πρόγνωσης (ατμοσφαιρικό, υδρολογικό, μοντέλο διάχυσης σωματιδίων) επιστρατεύονται προς αυτό το σκοπό.Μια νέα τεχνική προσαρμογής δορυφορικών μετρήσεων αναπτύχθηκε στα πλαίσια αυτής της διατριβής. Σε αυτή, τα δορυφορικά δεδομένα αξιοποιούνται ως προς την εκτίμηση της χωροχρονικής εξέλιξης των καταιγίδων, ενώ σε ότι αφορά την ποσότητα του υετού, οι εκτιμήσεις προσαρμόζονται στις αντίστοιχες του αριθμητικών μοντέλων πρόγνωσης. Κατ’ αυτόν τον τρόπο, η αξιόπιστη χωροχρονική επισκόπηση από τους δορυφόρους διατηρείται ενώ οι συχνά εσφαλμένες ποσότητες υετού των δορυφορικών οργάνων πάνω από ορεινές περιοχές διορθώνονται με τη χρήση των ατμοσφαιρικών προσομοιώσεων. Η διόρθωση των δορυφορικών δεδομένων λαμβάνει χώρα μέσω μιας μεθόδου πυκνότητας πιθανότητας. Η αξιολόγηση των πρωτογενών δορυφορικών δεδομένων, των αριθμητικών προσομοιώσεων και των τελικών υβριδικών προϊόντων γίνεται έναντι σε πυκνά δίκτυα βροχομέτρων και πεδία από διαθέσιμα μετεωρολογικά ραντάρ. Λαμβάνει δε χώρα σε τρεις ορεινές περιοχές με διαφορετικά χαρακτηριστικά: δύο μέσων γεωγραφικών πλατών (Άλπεις και Βραχώδη Όρη) και μια υποτροπική (Αιθιοπία).Οι προσομοιώσεις των αριθμητικών μοντέλων υποδεικνύουν τη φύση των περιορισμών στην ανίχνευση του υετού από τα δορυφορικά όργανα. Μια μικροφυσική διερεύνηση λαμβάνει χώρα και οι ομοιότητες που παρουσιάζουν οι εν λόγω καταιγίδες στις περιπτώσεις όπου η δορυφορική ανίχνευση εμφανίζει μεγάλες αποκλίσεις από τις παρατηρήσεις σχολιάζονται διεξοδικά. Παράλληλα, παρουσιάζεται μια εκτίμηση του οφέλους που μπορεί να προκύψει στο άμεσο μέλλον από την υιοθέτηση πολύ λεπτομερών χωρικών αναλύσεων στα αριθμητικά μοντέλα πρόγνωσης. Αποτελέσματα από προσομοιώσεις σε χωρικές κλίμακες μικρότερες του 1 χιλιομέτρου (σ.σ.: έως και 250 μέτρα) συγκρίνονται με αντίστοιχα από κλίμακες που αποτελούν τον τρέχοντα κανόνα στις μετεωρολογικές υπηρεσίες (1 και 4 χιλιόμετρα). Οι επιπτώσεις που προκαλούν αυτές οι διαφορές στην εκτίμηση του υετού από το ατμοσφαιρικό μοντέλο στην υδρολογία και συγκεκριμένα στην απορροή των υδάτων εξετάζονται μέσω αντίστοιχων προσομοιώσεων με υδρολογικό μοντέλο.Για τις ανάγκες της διατριβής χρησιμοποιήθηκαν ένα εξελιγμένο ατμοσφαιρικό αριθμητικό μοντέλο (RAMS/ICLAMS), ένα υδρολογικό μοντέλο (CREST) καθώς κι ένα λανγκρανζιανό μοντέλο διασποράς-διάχυσης (HYPACT). Το πρώτο καθόρισε την υετίσιμη ποσότητα σε κάθε καταιγίδα και παρείχε την πληροφορία για περαιτέρω ανάλυση σε επίπεδο μικροφυσικής νεφών, το δεύτερο εκτίμησε τις απορροές που προέκυψαν από τις ατμοσφαιρικές προσομοιώσεις και το τρίτο χρησίμευσε στον καθορισμό της προέλευσης των υγρών αερίων μαζών πάνω από περιοχές όπου η βιβλιογραφία δεν ήταν ιδιαίτερα εκτεταμένη. Δυο δορυφορικά προϊόντα, που βασίζονται σε διαφορετικές τεχνικές ανίχνευσης και συγκεκριμένα από αισθητήρες υπέρυθρου (IR) και μικροκυμάτων (PMW) χρησιμοποιήθηκαν προκειμένου να υποδείξουν τους περιορισμούς που χαρακτηρίζουν την κάθε μέθοδο ανίχνευσης πάνω από περιοχές έντονου αναγλύφου. Αμφότερα είναι προϊόντα υψηλής χωρικής ανάλυσης (4 και 8 χιλιόμετρα αντίστοιχα).Τα αποτελέσματα εμφανίζουν οφέλη από τις λεπτομερείς χωρικές κλίμακες των προσομοιώσεων, τόσο στις ποσότητες του υετού, στη λεπτομερέστερη χωρική του κατανομή, όσο και την ακριβέστερη εκτίμηση της απορροής στη συνέχεια. Οι δορυφορικές μετρήσεις εμφανίζουν μια ξεκάθαρη τάση υποεκτίμησης του υετού πάνω από περιοχές έντονου αναγλύφου. Τα διορθωμένα δορυφορικά προϊόντα που προέκυψαν από την προτεινόμενη μέθοδο, υπερτερούν έναντι των πρωτογενών στη στατιστική ανάλυση και στις δύο περιοχές εφαρμογής. Σε επίπεδο μικροφυσικών ομοιοτήτων μεταξύ των περιπτώσεων ανεπαρκούς ανίχνευσης από τα δορυφορικά όργανα, παρατηρήθηκαν μικρές συγκεντρώσεις σωματιδίων πάγου και νεφικοί σχηματισμοί με περιορισμένη κατακόρυφη ανάπτυξη. Η διόρθωση των δορυφορικών παρατηρήσεων μέσω των αριθμητικών προσομοιώσεων εμφανίζεται ως μια αξιόπιστη εναλλακτική σε περιοχές όπου οι παρατηρήσεις δεν είναι επαρκείς προς εξυπηρέτηση αυτού του σκοπού.Η συνεισφορά της παρούσης διατριβής έγκειται αφενός στην προετοιμασία του εδάφους για μελλοντικά υβριδικά προϊόντα υετού, αφετέρου στην ανίχνευση των μικροφυσικών ομοιοτήτων που εμφανίζουν οι καταιγίδες οι οποίες δεν ανιχνεύονται ικανοποιητικά από τα δορυφορικά όργανα. Το τελευταίο μπορεί να καθορίσει σημαντικά την ανάπτυξη των σύγχρονων αλγορίθμων ανίχνευσης από τους παθητικούς αισθητήρες μικροκυμάτων. Τέλος, η εφαρμογή της προτεινόμενης μεθοδολογίας σε ψευδο-επιχειρησιακή βάση κατά τη διάρκεια ενός ιστορικού πλυμμηρικού φαινομένου, παρέχει μια εκτίμηση της επιχειρησιακής εφαρμοσιμότητας και του συγκριτικού οφέλους που μπορεί να προκύψει από την υιοθέτηση της συγκεκριμένης μεθόδου σε συστήματα έγκαιρης πρόγνωσης και πρόληψης πλημμυρών.

scholarly journals Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

2016 ◽  
Vol 8 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Sunil Ghaju ◽  
Knut Alfredsen
Keyword(s):  
Bias Correction ◽  
Rain Gauge ◽  
Extensive Study ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Satellite Precipitation ◽  
Precipitation Estimates ◽  
Set Up ◽  
Rain Gauge Network ◽  
Precipitation Station

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

scholarly journals On the Uncertainty of High-Resolution Hourly Quantitative Precipitation Estimates in California

2020 ◽  
Vol 21 (5) ◽  
pp. 865-879
Author(s):  
Janice L. Bytheway ◽  
Mimi Hughes ◽  
Kelly Mahoney ◽  
Rob Cifelli
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Heavy Rain ◽  
Reference Dataset ◽  
Wet Season ◽  
Large Variability ◽  
Operational Forecast ◽  
Bay Area ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

AbstractThe Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area.

scholarly journals Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Basile Pauthier ◽  
Benjamin Bois ◽  
Thierry Castel ◽  
D. Thévenin ◽  
Carmela Chateau Smith ◽  
...  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Rain Gauge ◽  
Local Scale ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Preliminary Evaluation ◽  
Rainfall Variation ◽  
Hourly Rainfall ◽  
Rain Gauge Network

A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE) by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1) PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2) both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3) PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE). This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

2011 ◽  
Vol 12 (6) ◽  
pp. 1414-1431 ◽  
Author(s):  
David Kitzmiller ◽  
Suzanne Van Cooten ◽  
Feng Ding ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
...  
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Freezing Level ◽  
Distributed Hydrologic Model ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

scholarly journals Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

2021 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Intensity ◽  
Rain Gauge ◽  
Model Parameters ◽  
Rainfall Events ◽  
Precipitation Estimates ◽  
Attenuation Models ◽  
Using Data ◽  
Quantitative Precipitation Estimates ◽  
Rain Gauge Network ◽  
Commercial Microwave Links

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

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