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1. Deriving Gridded Hourly Rainfall on O‘ahu by Combining Gauge and Radar Rainfall

   https://doi.org/10.1175/JHM-D-22-0196.1  

   Huang, Yu-Fen ; Tsang, Yinphan ; Nugent, Alison D. ( December 2023 , Journal of Hydrometeorology)

   High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.

   The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.

    

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

   https://doi.org/10.5194/amt-14-2699-2021  

   Lee, Yoonjin ; Kummerow, Christian D. ; Ebert-Uphoff, Imme ( January 2021 , Atmospheric Measurement Techniques)

   Abstract. An ability to accurately detect convective regions isessential for initializing models for short-term precipitation forecasts.Radar data are commonly used to detect convection, but radars that providehigh-temporal-resolution data are mostly available over land, and the qualityof the data tends to degrade over mountainous regions. On the other hand,geostationary satellite data are available nearly anywhere and in near-realtime. Current operational geostationary satellites, the GeostationaryOperational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible andinfrared data even without vertical information of the convective system.Existing detection algorithms using visible and infrared data look forstatic features of convective clouds such as overshooting top or lumpy cloudtop surface or cloud growth that occurs over periods of 30 min to anhour. This study represents a proof of concept that artificial intelligence(AI) is able, when given high-spatial- and high-temporal-resolution data fromGOES-16, to learn physical properties of convective clouds and automate thedetection process. A neural network model with convolutional layers is proposed to identifyconvection from the high-temporal resolution GOES-16 data. The model takesfive temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) asinputs and produces a map of convective regions. In order to provideproducts comparable to the radar products, it is trained against Multi-RadarMulti-Sensor (MRMS), which is a radar-based product that uses a rathersophisticated method to classify precipitation types. Two channels fromGOES-16, each related to cloud optical depth (channel 2) and cloud topheight (channel 14), are expected to best represent features of convectiveclouds: high reflectance, lumpy cloud top surface, and low cloud toptemperature. The model has correctly learned those features of convectiveclouds and resulted in a reasonably low false alarm ratio (FAR) and highprobability of detection (POD). However, FAR and POD can vary depending onthe threshold, and a proper threshold needs to be chosen based on thepurpose. 

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3. Mapping, Monitoring, and Prediction of Floods Due to Ice Jam and Snowmelt with Operational Weather Satellites

   https://doi.org/10.3390/rs12111865  

   Goldberg, Mitchell D. ; Li, Sanmei ; Lindsey, Daniel T. ; Sjoberg, William ; Zhou, Lihang ; Sun, Donglian ( June 2020 , Remote Sensing)

   null (Ed.)

   Among all the natural hazards throughout the world, floods occur most frequently. They occur in high latitude regions, such as: 82% of the area of North America; most of Russia; Norway, Finland, and Sweden in North Europe; China and Japan in Asia. River flooding due to ice jams may happen during the spring breakup season. The Northeast and North Central region, and some areas of the western United States, are especially harmed by floods due to ice jams and snowmelt. In this study, observations from operational satellites are used to map and monitor floods due to ice jams and snowmelt. For a coarse-to-moderate resolution sensor on board the operational satellites, like the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System (JPSS) series, and the Advanced Baseline Imager (ABI) on board the GOES-R series, a pixel is usually composed of a mix of water and land. Water fraction can provide more information and can be estimated through mixed-pixel decomposition. The flood map can be derived from the water fraction difference after and before flooding. In high latitude areas, while conventional observations are usually sparse, multiple observations can be available from polar-orbiting satellites during a single day, and river forecasters can observe ice movement, snowmelt status and flood water evolution from satellite-based flood maps, which is very helpful in ice jam determination and flood prediction. The high temporal resolution of geostationary satellite imagery, like that of the ABI, can provide the greatest extent of flood signals, and multi-day composite flood products from higher spatial resolution imagery, such as VIIRS, can pinpoint areas of interest to uncover more details. One unique feature of our JPSS and GOES-R flood products is that they include not only normal flood type, but also a special flood type as the supra-snow/ice flood, and moreover, snow and ice masks. Following the demonstrations in this study, it is expected that the JPSS and GOES-R flood products, with ice and snow information, can allow dynamic monitoring and prediction of floods due to ice jams and snowmelt for wide-end users. 

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4. Spectrally simplified approach for leveraging legacy geostationary oceanic observations

   https://doi.org/10.1364/AO.465491  

   Houskeeper, Henry F. ; Hooker, Stanford B. ; Cavanaugh, Kyle C. ( September 2022 , Applied Optics)

   The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the$R$Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation,$K_d(\mathrm{P}\mathrm{A}\mathrm{R})$. First, anin situABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of$K_d(\mathrm{P}\mathrm{A}\mathrm{R})$were estimated by fitting the ratio of the shortest and longest visible wave bands from thein situABI dataset to coincident,in situ$K_d(\mathrm{P}\mathrm{A}\mathrm{R})$data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination ($R^2$) value (0.88) resulted in a root mean square difference of$0.319{\mathrm{m}}^{-<\#comment/>1}$, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability ($R^2=0.60$) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm,$K_d(490)$, values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

    

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5. Cloud Properties Derived From Airborne Cloud Radar Observations Collected in Three Climatic Regions

   https://doi.org/10.1029/2023JD039829  

   Romatschke, Ulrike ( October 2023 , Journal of Geophysical Research: Atmospheres)

   Clouds observed by the airborne High‐Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) were classified into twelve categories, based on their convective/stratiform nature. Dimensional and convective cloud properties were analyzed in three climatic regions: The subtropical easterlies off the coast of California, the Southern Ocean, and the tropics surrounding Central America. The convective properties of the stratocumulus clouds in the subtropical easterlies are closely related to the degree of boundary layer decoupling. In regions of strong boundary layer coupling, convectivity and updrafts in the clouds are weak and precipitation is light. In regions where the boundary layer is more decoupled, convective properties increase together with cloud top altitudes and cloud depth. Cloud properties of stratocumulus and cumulus clouds over the Southern Ocean show similarities to those observed in the subtropics, but overall they are less convective, indicating a strongly coupled boundary layer. Sea surface temperatures are much lower and the development of clouds is driven by transient synoptic conditions rather than zonal ocean temperature gradients. Clouds observed over the tropical oceans are much more convective in nature. As in the two other regions, they are mostly shallow, but clouds in regions with high sea surface temperatures have high convectivity and reflectivity values and stronger updrafts. Some of them grow to extreme depths (>14 km) and widths (>500 km). They have strong and large updraft regions and are heavily precipitating throughout their life cycle as they transition from the convective to the stratiform stage.

    

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