Remote Sensing of Orographic Precipitation

Barros, A. P.; Arulraj, Malarvizhi

doi:10.1007/978-3-030-35798-6_6

Cited by 19 publications

(18 citation statements)

References 58 publications

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“…Warm convective clouds tend to have a structure that promote a downward increase in the radar reflectivity, regarded as a bottom-heavy precipitation structure (e.g., [6,11,22,68]); this is due to the dominance of the collision-coalescence process, which leads to difficulties in estimating rainfall while using radar due to the ground clutter. The shallowness of the clouds also causes difficulty in detecting precipitation over complicated topographies [12]. Terao et al (2017) [69] validated the near-surface rain dataset of the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) using tipping-bucket rain gauges over northeast India and Bangladesh; they identified significant underestimation of the TRMM PR near surface rainfall over the Meghalaya Plateau and adjacent Bangladeh plain.…”

Section: Summary and Discussionmentioning

confidence: 96%

“…Warm convective clouds have bottom-heavy precipitation structures [11] due to the dominance of the coalescence process. In addition, shallow clouds over complicated topographies tend to cause ground-clutter contamination of reflectivity profiles, non-uniform beam filling artifacts, and parallax errors; thus, remote sensing under these conditions typically misses or underestimates the precipitation [12].…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

et al. 2020

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The rain drop size distribution (DSD) at Cherrapunji, Northeast India was observed by a laser optical disdrometer Parsivel 2 from May to October 2017; this town is known for the world’s heaviest orographic rainfall recorded. The disdrometer showed a 30% underestimation of the rainfall amount, compared with a collocated rain gauge. The observed DSD had a number of drops with a mean normalized intercept log 10 N w > 4.0 for all rain rate categories, ranging from <5 to >80 mm h − 1 , comparable to tropical oceanic DSDs. These results differ from those of tropical oceanic DSDs, in that data with a larger N w were confined to the stratiform side of a stratiform/convective separation line proposed by Bringi et al. (2009). A large number of small drops is important for quantitative precipitation estimates by in-situ radar and satellites, because it tends to miss or underestimate precipitation amounts. The large number of small drops, as defined by the second principal component (>+1.5) while using the principal component analysis approach of Dolan et al. (2018), was rare for the pre-monsoon season, but was prevalent during the monsoon season, accounting for 16% (19%) of the accumulated rainfall (precipitation period); it tended to appear over weak active spells or the beginning of active spells of intraseasonal variation during the monsoon season.

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Section: Summary and Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

et al. 2020

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“…The frequency of small midday PFs doubles in February for mostly light stratiform rainfall. The high number of small raindrops at midday (Figure 4) is indicative of warm rain processes possibly including SFI among layered shallow clouds, which might be misclassified as an indication of convective activity depending on overpass geometry similar to what happens in the Southern Appalachians (Arulraj & Barros, 2019;Barros & Arulraj, 2020;Duan et al, 2015).…”

Section: 1029/2020jd032947mentioning

confidence: 99%

“…Retrieval algorithms cannot detect precipitation below the clutter-free height (i.e., the blind zone). There are a high number of missed detections, while rain-rate estimates are significantly lower than ground-based observations (Barros & Arulraj, 2020;Duan et al, 2015). Furthermore, retrieval algorithms that rely on the presence of a bright band to classify precipitation as stratiform fail if the 0°isotherm is below the clutter-free height (Awaka et al, 2009).…”

Section: Diurnal Cyclementioning

confidence: 99%

“…For example, in the Central Andes of Peru's foothills, satellite‐based precipitation products (e.g., Tropical Rainfall Measurement Mission, TRMM 3B42) can underestimate annual rainfall up to 300% on average (Lowman & Barros, 2014). Whereas new products (e.g., IMERG; Huffman et al, 2015) using Global Precipitation Mission (GPM) observations show substantial improvements over the TRMM era, significant underestimation errors in mountainous regions tied to ground clutter artifacts remain (Barros & Arulraj, 2020).…”

Section: Introductionmentioning

confidence: 99%

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High‐Elevation Monsoon Precipitation Processes in the Central Andes of Peru

2020

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Measurements at the high-elevation Lamar Observatory in the Mantaro Valley (MV) in the Central Andes of Peru demonstrate a diurnal cycle of precipitation characterized by convective rainfall during the afternoon and nighttime stratiform rainfall with embedded convection. Wet season data (2016-2018) reveal long-duration (6-12 hr) shallow precipitating systems (LDPS) that produced about 17% of monsoon rainfall in 2016 and 2018 associated with El Niño and La Niña, respectively. The LPDS fraction of monsoon rainfall doubles to 35% with weekly recurrence in 2017 under El Niño Costero (coastal) conditions. LDPS occur under favorable moisture conditions dictated by the South America (SA) Low-Level Jet (SALLJ) and Cold Air Intrusions (CAIs). Backward trajectory analysis shows that precipitable water sustains >80% of seasonal precipitation and ties the LPDS to particular moisture source regions in the eastern Andes foothills 1-2 days in advance, enhanced by increased moisture supply in the midtroposphere. Higher frequency of CAIs and enhanced midlevel moisture convergence along CAI fronts explain the increased LDPS frequency during the 2017 El Niño Costero. These findings highlight the functional role of the Andes morphology in organizing moisture supply to high-elevation precipitation systems on the orographic envelope. Analysis of the Global Precipitation Measurement (GPM) mission satellite-based radar observations points to challenges to precipitation detection and estimation in this region as the GPM clutter-free height (~1-2 km AGL) exceeds the depth of shallow precipitation systems in the MV.

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Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Satyaningsih,

Jetten,

Ettema

et al. 2023

Nat Hazards

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This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

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Remote Sensing of Orographic Precipitation

Cited by 19 publications

References 58 publications

Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

Characteristics of Orographic Rain Drop-Size Distribution at Cherrapunji, Northeast India

High‐Elevation Monsoon Precipitation Processes in the Central Andes of Peru

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

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