Science Highlights

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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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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