NOAA Center for Earth System Sciences and Remote Sensing Technologies

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms is expected to increase, and as built environments impede the absorption of water, the threat of loss of human life and property damages exceeding billions of dollars are heightened. Hence, agencies and organizations are implementing novel modeling methods to combat the consequences. This review details the concepts, impacts, and causes of urban flooding, along with the associated modeling endeavors. Moreover, this review describes contemporary directions towards urban flood resolutions, including the more recent hydraulic-hydrologic models that use modern computing architecture and the trending applications of artificial intelligence/machine learning techniques and crowdsourced data. Ultimately, a reference of utility is provided, as scientists and engineers are given an outline of the recent advances in urban flooding research.

Abstract We compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.

Sea surface temperatures and vertical wind shear are essential to tropical cyclone formation. TCs need warm SSTs and low shear for genesis. Increasing SSTs and decreasing VWS influences storm development. This work analyzes SST and VWS trends for the Caribbean, surrounding region, and the Atlantic hurricane main developing region from 1982–2020. Storm intensity increases significantly during this period. Annual and seasonal trends show that regional SSTs in the MDR are warming annually at 0.0219 °C yr−1 and, per season, 0.0280 °C yr−1. Simultaneously, VWS decreases during the late rainfall season, at 0.056 m/s yr−1 in the MDR and 0.0167 m/s yr−1 in the Caribbean and surrounding area. The Atlantic Warm Pool is expanding at 0.51 km2 per decade, increasing upper atmospheric winds and driving VWS changes. Correlations of large-area averages do not show significant relationships between TC intensity, frequency, and SSTs/VWS during the LRS. The observed changes appear to be associated with regional warming SSTs impacting TC changes. Plain Language Abstract: Tropical cyclone (TC) formation requires warm ocean waters and low wind shear. Changes to sea surface anomalies and wind shear influences are essential to understanding storm development and intensification. The ability to forecast storm changes is vital to human lives and livelihoods. This work analyzes sea surface temperatures (SSTs) and vertical wind shear (VWS) trends in the Caribbean, surrounding areas, and the Atlantic main developing region (MDR). We found increasing SSTs, decreasing wind shears, an expanding Atlantic Warm Pool (AWP), and increased storm intensity during the Atlantic hurricane season.

Planetary-boundary-layer-height (PBLH) plays a critical role in the study of urban air quality, weather and climate. Continuous observation is critical in understanding air pollution processes and evaluation of air quality/ weather models in the complex urban environment. In this study, we observe the PBLH variation using multiple ceilometers and lidar in New York City (NYC) during both the summer and winter time and explore the potential correlation with ground PM2.5. An automated quality control and quality assurance (QC/QA) method is developed to optimize the PBLH determination from the ceilometers (Vaisala CL51 and Lufft CHM15k) product. The PBLHs from the two ceilometers and lidar show good consistency (R 2 =0.68~0.88) during the convective PBL period at 15:00-21:00 UTC (10:00-16:00 EST). We also investigate the seasonal variation and diurnal evolution of PBLH and demonstrate an inverse relation between the PBLH and PM2.5 during the morning transient period of PBLH growth. Further, the correlation between the ceilometer-attenuated backscatter and ground PM2.5 and its dependences on the vertical altitude are analyzed, showing that the aerosols in the PBL are more deeply mixed while also being influenced by the relatively high humidity variability during the summer.

The purpose of this study is to estimate high temporal and high spatial resolution land surface temperature (LST) over different surface types in urban regions. The goal is to estimate high resolution LST by combining Landsat 8 and the Geostationary Operational Environmental Satellite-R Series (GOES-R) infrared-based LST. Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, GOES-R which has lower spatial resolution (2 km) has much higher temporal resolution (5 min). The research project aims to match the dates that both GOES-R and Landsat LSTs to find their spatial relationship to develop the downscaling of GOES-R LST. The downscaling approach will account for systematic biases between Landsat and GOES-R LST products.

The Advanced Technology Microwave Sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting, climate research, and help us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This study proposes a method to enhance the imagery visualization for ATMS, combining limb correction with artificial intelligence (AI) resolution enhancement. Measurement data from the ATMS onboard NOAA-20 was utilized to train the limb-correction method, which was then validated using newly acquired NOAA-21 ATMS data. The AI resolution enhancement was performed using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), which increased the pixel resolution by a factor of four. The high-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) data served as a reference to initially and quantitatively evaluate the resolution enhancement method. The combined method of limb correction and AI resolution enhancement produced an angular-dependence-free and high-resolution ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal high-resolution imagery visualization system – JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release .

High ozone (O 3 ) episodes frequently occur in New York metropolitan and the downwind coastal area in summer. In this study, lidar/ceilometer are combined with WRF/Chem model to investigate an O 3 event on Aug. 27~30 2018. We examine the spatial-temporal variabilities of O 3 and planetary-boundary-layer height (PBLH) and assess the model performance on simulating surface O 3 during this episode. By comparing with the lidar observations, the WRF/Chem is able to capture high O 3 distribution in the PBL at noon and indicates consistent diurnal evolution for the ground O 3 . Nevertheless, in the early morning and night, the model overestimates the ground O 3 and underestimates the PBLH.

Abstract. Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected eleven sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. The functions contain helpful optional parameters while maintaining standard practices between calculations. After documenting the kernels and ClimKern package, we test it with sample climate model output to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread shows considerable spatial heterogeneity, with the greatest spread in the Arctic and over the Southern Ocean. Considerable sensitivity to kernel choice is found even in the global means, with the surface albedo and cloud feedbacks showing the greatest spread across different kernels. Our results highlight the importance of using more than one radiative kernel and standardizing feedback calculations, like those offered by ClimKern, in climate feedback, climate sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it even more useful to the feedback community.

The directory includes the required information for running the exploratory analysis and Bayesian model.

The directory includes the required information for replicating the analysis

The directory includes the required information for replicating the analysis

The directory includes the required information for running the exploratory analysis and Bayesian model.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected eleven sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. The functions contain helpful optional parameters while maintaining standard practices between calculations. After documenting the kernels and ClimKern package, we test it with sample climate model output to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread shows considerable spatial heterogeneity, with the greatest spread in the Arctic and over the Southern Ocean. Considerable sensitivity to kernel choice is found even in the global means, with the surface albedo and cloud feedbacks showing the greatest spread across different kernels. Our results highlight the importance of using more than one radiative kernel and standardizing feedback calculations, like those offered by ClimKern, in climate feedback, climate sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it even more useful to the feedback community.

Air pollution associated with wildfire smoke transport and heat wave in summer pose serious public health concerns in the populated New York City (NYC) area. In this study, we present a synergistic lidar, ceilometer and in-situ observation for wildfire smoke transport and planetary-boundary-layer (PBL) variation in the NYC urban and coastal area during the summer 2018 Long Island Sound Tropospheric Ozone (O 3 ) Study (LISTOS). A dense smoke plume and mixing into PBL on August 15-17, 2018 was analyzed while the coincident enhancement of PM 2.5 , CO and O 3 exceedance of NAAQS was demonstrated from both the observation and model. In addition, we show the temporal-spatial variation and difference of the PBL-height (PBLH) in the NYC urban and its coastal vicinity. We further evaluate the NAM-CMAQ model forecast of O 3 , PM 2.5 and PBLH with the ground observations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected eleven sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. The functions contain helpful optional parameters while maintaining standard practices between calculations. After documenting the kernels and ClimKern package, we test it with sample climate model output to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread shows considerable spatial heterogeneity, with the greatest spread in the Arctic and over the Southern Ocean. Considerable sensitivity to kernel choice is found even in the global means, with the surface albedo and cloud feedbacks showing the greatest spread across different kernels. Our results highlight the importance of using more than one radiative kernel and standardizing feedback calculations, like those offered by ClimKern, in climate feedback, climate sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it even more useful to the feedback community.