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If you are a Student or Early Career Scientist working on precipitation and you want to see your last paper highlighted here and on our social media, fill this form. We will post one Science Highlight every month selecting the paper based on the submission time of the application.

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April 2023 - Lee et al. 2022

Latent heating profiles from GOES-16 and its impacts on precipitation forecasts

Atmospheric Measurement Techniques. https://doi.org/10.5194/amt-15-7119-2022

Vertical profiles of latent heating are derived from GOES-16 to be used for convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.

Tags: #latentheating #precipitation #forecast #convection #GOES16

March 2023 - Sharannya et al. 2020

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Water. https://doi.org/10.3390/w12092400

The TRMM precipitation data underestimates the water availability for agriculture purposes corresponding to Q70 dependable flow, whereas the CHIRPS rainfall data are capable of capturing flow quantiles produced by the IMD gridded data. At the high altitude and forest areas, TRMM underestimates rainfall and overestimates for the urban areas. The CHIRPS-0.25 rainfall produces a similar pattern of flow quantiles of IMD than CHIRPS-0.05, which conveys that the improvement in spatial resolution does not cause any significant improvement in the flow quantiles, the key hydrological signature. Hence, it could be concluded that the uncertainties in the model performance and parameters critically depend on the selection of precipitation datasets. Different sets of data exhibit different results which may be a cause of concern while making appropriate decisions related to water management practices. Hence, it is recommended to choose appropriate rainfall data depending on the application.

Tags: #CHIRPS #FDC #hydrological signature #SWAT #TRMM #Western Ghats

February 2023 - Choudhury et al. 2020

A diagnostic study of cloud physics and lightning flash rates in a severe pre‐monsoon thunderstorm over northeast India

Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.3773

The thunderstorms occurring during the pre monsoon seasons over NEI and the adjoining regions are extremely deep convective clouds with minimum cloud-top temperature −70 °C at 19 km. They are triggered by the mixing of moist air mass transported from the Bay of Bengal in the south and dry air transported from the northwest. The cyclonic circulation observed over the Tibetan plateau acting as heat low is likely to be associated with the strong southerly low-level wind over the region. The coexistence of ice particles and supercooled water in the storms resulted in a large number of lightning flashes as observed from the TRMM-LIS. Co-location of supercooled cloud water droplets helps in forming graupel through riming that plays a vital role in these convective systems, explains the simulation results. ARW model could simulate the thunderstorm very well, hence is recommended to use this state-of-the-art regional model in thunderstorm and lightning predictions for northeast India and adjoining regions which would be useful in preparedness for such extreme events.

Tags: thunderstorms; convection; convective_systems; wettest_place_on_earth; NortheastIndia; TRMM; ARW

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November 2022 - Watters et al. 2021

The Diurnal Cycle of Precipitation according to Multiple Decades of Global Satellite Observations, Three CMIP6 Models, and the ECMWF Reanalysis

Journal of Climate. https://doi.org/10.1175/JCLI-D-20-0966.1

Identifying and addressing climate model errors in representing the diurnal cycle of precipitation are critical to improving their accuracy. This study provides an update on the diurnal cycle performance of state-of-the-art climate models and reanalysis against state-of-the-art satellite observations. The models and reanalysis have varying biases in diurnal amplitude over land, where amplitudes are stronger, and they underestimate amplitudes over ocean. They also simulate precipitation over land to peak too early in the day, from −1 to −4 h on average depending on the model. Nocturnal maxima in mountainous regions are not well simulated, although the reanalysis outperforms the models in this case. Future work can use these findings to improve realism in the next generation of climate models.

Tags:DiurnalCycle; Precipitation; GPM; IMERG; ERA5; CMIP6; SatelliteObservations; Reanalysis; ClimateModels.

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October 2022 - Eldardiry et al. 2020

Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis

Remote Sensing. https://doi.org/10.3390/rs12223767

Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches

Tags: rainfall; radar; extreme precipitation; spatial bootstrap; Louisiana; annual maxima

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September 2022 - Saha et al. 2021

Assessment of OTT Pluvio2 Rain Intensity Measurements

Journal of Atmospheric and Oceanic Technology. https://doi.org/10.1175/JTECH-D-19-0219.1

Accurate and precise measurement of rainfall parameters is one of the essential input variables of many environmental issues such as weather pattern change predictions, quantitative precipitation forecasts (QPF), rainfall intensity–duration–frequency (IDF) curves’ derivation, and rainwater harvesting planning. This study evaluates the real time rain intensity measurements by OTT Pluvio2 under both static and dynamic conditions. Also, we assessed OTT Pluvio2 rain intensity measurement errors induced by bucket weight response time. Laboratory experiments considering both static and dynamic RI revealed that the lower threshold for the OTT Pluvio2’s RI measurements should be revised. Moreover, results from dynamic in situ scenarios express the potentiality of large errors in Pluvio2 RI measurements due to time delay in bucket weight measurement and deficient of the Pluvio2 internal algorithm.

Tags: Atmosphere; Precipitation; In situ atmospheric observations; Instrumentation/sensors; Measurements; Error analysis

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August 2022 - Kolluru et al. 2020

Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India
Remote Sensing. https://doi.org/10.3390/rs12183013

Sixteen machine learning algorithms (MLAs) were applied on three secondary precipitation products (SPPs) to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. This study provides an insight to the researchers to select suitable MLAs for integrating multiple precipitation datasets along with the best dataset that can be employed in any region for hydrological and climatological applications. This study is highly useful in ungauged catchments or basins having poor meteorological gauging network.

Tags: rainfall; machinelearning; precipitation; ScienceHighlights; MDPI; remotesensing

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July 2022 - Mangla et al. 2020

Evaluation of convective storms using spaceborne radars over the Indo-Gangetic Plains and western coast of India
Meteorological Applications. https://doi.org/10.1002/met.1917

This study investigates radar reflectivity signatures on convective clouds using GPM Precipitation Features during the monsoonal seasons of years 2014-2017. Three types of clouds are defined: Convective Tower, intense convective clouds at 8 Km, and intense convective clouds at 3 km over the Indo-Gangetic Plains (IGP) and Indian Western coastal (WG) region. Results show that regional variations are more enhanced for intense convective clouds with high occurrence over IGP region. Further, the aerosol-cloud interaction is examined for warm and mixed-phase clouds. The vertical structure of aerosols show suppression of warm rainfall over IGP region. However, rainfall intensity is known to increase in mixed-phase clouds due to dominancy of ice processes. The significant positive (negative) correlation is observed between the echo top height and aerosol concentrations over the IGP (WG) region. The combination of both Ku and W band space borne radars reveals great potential for the investigation of cloud-aerosol interactions.

Tags: GPM; Precipitation-features; Aerosol-cloud-precipitation

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June 2022 - Krell et al. 2021

Consequences of Dryland Maize Planting Decisions Under Increased Seasonal Rainfall Variability
Water Resources Research. https://doi.org/10.1029/2020WR029362

Shifts in rainfall frequency and intensity can lead to heavy crop loss in rainfed agricultural systems. To better understand the interactions between rainfall variability, cultivar choice, and cropping success, we implement an ecohydrological model that accounts for variation in daily soil moisture and converts water stress to crop yield. We apply the model to growing conditions of dryland farmers in central Kenya, which is a drought-prone and semiarid region with spatially heterogeneous rainfall. We show that maize crops are prone to water deficit in the part of the growing season when crop water requirements are highest. Despite the potential for higher-yielding, late maturing varieties to improve total harvest, we find that early maturing varieties that are drought-avoidant have the lowest likelihood of failure. In light of reduced rainfall totals, we show that the historical probability of crop failure was lowest in the past and is now increasing.

Tags: Rainfall variability; Maize production; Ecohydrological model; Dryland farmers

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May 2022 - Garg et al. 2021

Diurnal cycle of Tropical Oceanic Mesoscale Cold Pools
Journal of Climate. https://doi.org/10.1175/JCLI-D-20-0909.1

The global diurnal cycle of oceanic convectively generated cold pools (cool outflows from convection) is observed for the first time using the gradient feature method—revealing a bimodal distribution of cold pools organized by deep, organized convective systems in the early morning and afternoon cold pools predominated by shallower, more isolated convection. This analysis will provide a reference for high-resolution climate models to mimic in order to accurately represent key processes that organize convection and govern air–sea interactions.

Tags: Mesoscale processes; Atmosphere-ocean interaction; Cold pools; Diurnal effects; Precipitation

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April 2022 - Brunner et al. 2022

An extremeness threshold determines the regional response of floods to changes in rainfall extremes
Communications Earth & Environment. https://doi.org/10.1038/s43247-021-00248-x

Precipitation extremes will increase in a warming climate, but the response of flood magnitudes to heavier precipitation events is less clear. Here we investigate how flood magnitudes change in response to warming, using a large initial-condition ensemble of simulations with a single climate model, coupled to a hydrological model. The model chain was applied to historical (1961–2000) and warmer future (2060–2099) climate conditions for 78 watersheds in hydrological Bavaria, a region representing an area of expressed hydrological heterogeneity. For the majority of the catchments, we identify a ‘return interval threshold’ in the relationship between precipitation and flood increases: at return intervals above this threshold, further increases in extreme precipitation frequency and magnitude clearly yield increased flood magnitudes; below the threshold, flood magnitude is modulated by land surface processes. We suggest that this threshold behavior can reconcile climatological and hydrological perspectives on changing flood risk in a warming climate.

Tags: #Flood #Precipitation #Extremes #Warmer Climate

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March 2022 - Li et al. 2022

Evaluation of GPM IMERG and its constellations in extreme events over the conterminous United States
Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2021.127357

Improved quantification of extreme precipitation rates has far-reaching implications for environmental sciences. Yet, uncertainties remain in satellite precipitation estimates, especially for a merged product. This study evaluates the performance of the IMERG in extreme events over the conterminous US based on three methods: (1) a percentile-based analysis, (2) an event-based analysis using the NWS storm database, and (3) a frequency-based analysis using intensity–duration-frequency curves. The results reveal that: (1) three types of extreme definitions converge toward an overall agreement - the degrees of underestimation of high-end extreme precipitation rates increases with data latency; (2) passive microwave (PMW) estimates generally exhibits better detectability and quantification of extreme precipitation than Infrared (IR) estimates; (3) Amongst PMW sensors, MHS (SAPHIR)-based estimates show the best (worst) extreme detection, while AMSR and SSMIS outperform others for quantifying extreme rates. These findings reveal that IMERG is not a homogeneous precipitation product when estimating extreme precipitation.

Tags: #GPM #IMERG #Precipitation #Extremes

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February 2022 - Hayden et al. 2021

Properties of Mesoscale Convective Systems Throughout Their Lifetimes Using IMERG, GPM, WWLLN, and a Simplified Tracking Algorithm.
Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2021JD035264

Large-scale precipitation systems are important to the local and global climate. It is important to understand their lifecycle better for better inclusion in weather and climate models. We have developed a simple method to track these systems as they develop so that their lifecycle can be studied.

Tags: Precipitation; Convective Systems; IMERG; Lightning; Storm Tracking

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January 2022 - Venkadesh and Pazhanivelan 2021

Validation of PERSIANN Precipitation Product Using TAWN Rain Gauge Network Over Different Agro-climatic Zones in Tamil Nadu.
Madras Agricultural Journal (MAJ), https://doi.org/10.29321/MAJ.10.000513

PERSIANN rainfall product has good agreement with rain-gauges data of Tamil Nadu Agricultural Weather Network (TAWN). It was observed that PERSIANN is accurate in the high-altitude hilly zone and the Cauvery delta zone across Tamil Nadu, India. For 2015, 2016, and 2017, the correlation values were 0.77, 0.52, and 0.71, respectively. The highest RMSE value was measured for northeast zone (NEZ) during 2015 (222.17 mm), and the lowest was determined for 22.63 in the High-altitude hilly zone (HAHZ) during 2016 and NRMSE had less errors during all three seasons.

Tags: Validation; PERSIANN; Rain-gauge; Agro-Climatic zones; Tamil Nadu

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December 2021 - Mamalakis et al. 2021

Zonally contrasting shifts of the tropical rain belt in response to climate change.
Nature Climate Change, https://doi.org/10.1038/s41558-020-00963-x

Future changes in the position of the tropical rain belt with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the tropical rain belt, uncertainties remain large regarding its future position, with most past work focusing on global-average shifts. Here we use projections from 27 state-of-the-art climate models and document a robust longitudinally-varying response of the tropical rain belt to climate change by the year 2100, with a northward shift over eastern Africa and the Indian Ocean and a southward shift in the eastern Pacific and Atlantic oceans. The varying response is consistent with changes in the atmospheric energy transport and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rain belt and may allow for more robust projections of climate change impacts.

Tags: climatechange; climatechangeimpacts; rainfall; tropicalrainbelt; ITCZshifts; tropics; foodsecurity; drought; flooding

Social media handles: Antonis Mamalakis (Facebook), @tonismamal (instagram), @AntoniosMamala2 (twitter)

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November 2021 - Lee et al. 2021

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Atmospheric Measurement Techniques (AMT), https://doi.org/10.5194/amt-14-2699-2021

Visible and infrared data from geostationary satellites are available nearly anywhere and in near-real time, but it has been challenging to detect convection using those data due to the lack of vertical information. However, the current operational geostationary satellite, GOES-16 provides data in high spatial and temporal resolutions, and we can easily identify convection by looking at an image loop from GOES-16. In order to mimic how humans identify convection from GOES-16 images, a neural network model with convolution layers is developed. Inputs are five channel 2 reflectance data with two-minute interval and five channel 14 brightness temperature data with two-minute interval, and the model is trained against radar products called Multi-Radar Multi-Sensor (MRMS). Adaptive loss function is tested along with the standard loss function. Results show that the model was able to detect convection based on high reflectance, low brightness temperature, and lumpy textures in reflectance data.

Tags: machinelearning; ML; CNN; GOES16; ABI; convection; precipitation

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October 2021 - Brauer et al. 2021

The Inland Maintenance and Re-intensification of Tropical Storm Bill (2015) Part 2: Precipitation Microphysics
Hydrometeorology, https://doi.org/10.1175/JHM-D-20-0151.1

Tropical Storm Bill added to the catastrophic urban and river flooding that occurred over Oklahoma and Texas during May and June 2015 as it tracked over anomalously moist soils. This resulted in two periods of tropical cyclone maintenance and re-intensification (TCMI) over southern Oklahoma and Illinois. The associated precipitation processes were investigated using ground and space-borne radar observations during both TCMI events, and were found to be consistent with tropical cyclones even hundreds of kilometers inland from the landfall point. The dynamics were also analyzed and showed signatures consistent with large amounts of latent heat release during each TCMI period.

Tags: Hydrometeorology; Radar Microphysics; Tropical Cyclones

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September 2021 - Capecchi et al. 2021

Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water, https://doi.org/10.3390/w13131727

This study introduces a novel quantitative precipitation forecast (QPF) verification method based on the minimization of the root mean square error (RMSE) between the observed precipitation field and the optimally rotated and translated forecasted precipitation field. The method was applied on multiple simulations of the WRF model configuration operational at the meteorological center of the Tuscany region (LaMMA) for the prediction of a 200+ year return period heavy precipitation event. The simulations were: control run C; weather station data assimilation until 3 hours before the event S; weather station and radar data assimilation before the event S+R; weather station and radar data assimilation before the event and simplified ocean model S+R+M. Forecast validation shows tremendous improvements in the critical success index (CSI), computed for different precipitation thresholds, which jumps from values ~0 for the C and S runs to values up to 0.8 for runs S+R and S+R+M.

Tags: Precipitation; Radar; WRF; Assimilation; Forecast; Verification; LaMMA; UConn EEC

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August 2021 - Upadhyaya et al. 2021

Classifying precipitation from GEO satellite observations: Prognostic model

QJRMS, https://doi.org/10.1002/qj.4134

The new generation of geostationary Earth orbit (GEO) satellites provide high-resolution observations and opportunities to improve our understanding of precipitation processes. This study contributes to improved precipitation characterization and retrievals from space by identifying precipitation types (e.g., convective and stratiform) with multispectral observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES-16 satellite . The developed machine learning based model yields promising results in identifying the occurrence/non-occurrence with accuracy of 93%. Hail and warm stratiform types have high detection scores above 70%. Challenges exist in separating convective types. The study recommends to utilize probabilities instead of deterministically separating precipitation types, especially in regions with uncertain classifications.

Tags: Precipitation; Machine Learning; Satellites; GOES16; ABI

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