More Like this

1. Examining the Role of the Land Surface on Convection Using High‐Resolution Model Forecasts Over the Southeastern United States

   https://doi.org/10.1029/2022JD036563  

   Henderson, David S. ; Otkin, Jason A. ; Mecikalski, John R. ( August 2022 , Journal of Geophysical Research: Atmospheres)

   The influence of the Unified Noah and Noah‐MP land surface models (LSMs) on the evolution of cumulus clouds reaching convective initiation (CI) is assessed using infrared brightness temperatures (BT) from GOES‐16. Cloud properties from individual cloud objects are examined using output from high‐resolution (500 m horizontal grid spacing) model simulations. Cloud objects are tracked over time and related to observed clouds reaching CI to examine differences in cloud extent, longevity, and growth rate. The results demonstrate that differences in assumed surface properties can lead to large discrepancies in the net surface radiative budget, particularly in the sensible and latent heating components where differences exceed 40 W m⁻². These differences lead to changes in the local mesoscale circulation patterns that are more pronounced near the edges of forested and grassland boundaries where lower‐level convergence is stronger. Higher sensible heating from the Noah‐MP LSM produced growth of CI clouds earlier and with increased longevity, which was closer to the timing and growth observed from GOES‐16. The increased cloud growth in the Noah‐MP experiment results from stronger and deeper updrafts, which lofts more cloud water into the upper levels of the troposphere. The weaker updrafts from the Noah LSM experiment results in shallower convection after CI is detected due to slower growth rates. The differences in cloud properties and growth are directly related to the land surfaces they develop above and point to the importance of accurately representing land properties and radiative characteristics when simulating convection in numerical weather prediction models.

    

   more » « less
2. Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

   https://doi.org/10.1175/MWR-D-20-0272.1  

   Henderson, David S. ; Otkin, Jason A. ; Mecikalski, John R. ( April 2021 , Monthly Weather Review)

   The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-minGOES-16imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

    

   more » « less
3. Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

   https://doi.org/10.1029/2021MS002665  

   Abolafia‐Rosenzweig, Ronnie ; He, Cenlin ; Burns, Sean P. ; Chen, Fei ( November 2021 , Journal of Advances in Modeling Earth Systems)

   The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

    

   more » « less
4. 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. 

   more » « less
5. Modeling Large‐Scale Heatwave by Incorporating Enhanced Urban Representation

   https://doi.org/10.1029/2021JD035316  

   Patel, Pratiman ; Jamshidi, Sajad ; Nadimpalli, Raghu ; Aliaga, Daniel G. ; Mills, Gerald ; Chen, Fei ; Demuzere, Matthias ; Niyogi, Dev ( January 2022 , Journal of Geophysical Research: Atmospheres)

   This study evaluates the impact of land surface models (LSMs) and urban heterogeneity [using local climate zones (LCZs)] on air temperature simulated by the Weather Research and Forecasting model (WRF) during a regional extreme event. We simulated the 2017 heatwave over Europe considering four scenarios, using WRF coupled with two LSMs (i.e., Noah and Noah‐MP) with default land use/land cover (LULC) and with LCZs from the World Urban Database and Access Portal Tools (WUDAPT). The results showed that implementing the LCZs significantly improves the WRF simulations of the daily temperature regardless of the LSMs. Implementing the LCZs altered the surface energy balance partitioning in the simulations (i.e., the sensible heat flux was reduced and latent heat flux was increased) primarily due to a higher vegetation feedback in the LCZs. The changes in the surface flux translated into an increase in the simulated 2‐m relative humidity and 10‐m wind speed as well as changed air temperature within cities section and generated a temperature gradient that affected the temperatures beyond the urban regions. Despite these changes, the factor separation analysis indicated that the impact of LSM selection was more significant than the inclusion of LCZs. Interestingly, the lowest bias in temperature simulations was achieved when WRF was coupled with the Noah as the LSM and used WUDAPT as the LULC/urban representation.

    

   more » « less