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Jun Li
,
W. Paul Menzel
,
Timothy J. Schmit
, and
Johannes Schmetz

Abstract

A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China’s Fengyun-4 series since 2016 and will be on board Europe’s Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications.

Free access
Dong Ji
and
Fangli Qiao

Abstract

The validity of the gradient wind balance in a tropical cyclone (TC) remains controversial, especially for the boundary layer and upper outflow layer, even though this balance is assumed in the derivation of the Sawyer–Eliassen (SE) equation. This study derives an extended SE equation with the relaxation of the gradient wind and hydrostatic balance in cylindrical r–z coordinates, and then we diagnose the secondary circulation using this unbalanced SE equation and the azimuthally averaged tangential wind and thermodynamical fields from a three-dimensional numerical simulation of an intensifying TC. The gradient wind and hydrostatic imbalance produce two additional time-dependent forcing terms on the right-hand side (RHS) of SE equation which are proved to be negligible, even as the storm evolves rapidly. The use of the unbalanced basic state deforms the fields of coefficients that appear in the SE equation, and thus the forced secondary flows. The results indicate that the unbalanced solution captures the boundary-layer inflow better than the balanced solution described by Bui et al. (2009) and the pseudobalanced solution described by Heng et al. (2017). The unbalanced solution is closer to the simulation because more unbalanced components are included. Many previous studies, such as Heng et al. (2017), always employ the thermal wind balance relation to simplify the SE equation, which is invalid in unbalanced vortex and result in an overestimation of the boundary-layer inflow. This unbalanced dynamics could provide a reliable diagnosis of the secondary flow near the boundary layer.

Restricted access
Humberto Vergara
,
Jonathan J. Gourley
, and
Michael Erickson

Abstract

Uncertainty in Quantitative Precipitation Forecasts (QPFs) from Numerical Weather Prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the Neighboring Pixel Ensemble Technique (NPET) was devised and implemented as a post-processing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixels’ physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the United States National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context.

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Marie-Pier Labonté
and
Timothy M. Merlis

Abstract

Hydroclimatic extremes, such as heavy daily rainfall and dry spells, are expected to intensify under anthropogenic warming. Often, these changes are diagnostically related to thermodynamic increases in humidity withwarming. Here,we develop a framework that uses an online calculation of the thermodynamically induced changes of the full precipitation distribution with warming in an idealized moist atmospheric general circulation model. Two water vapor variables, the standard active one and an additional passive one (i.e., no latent heat release when condensation occurs), are advected by the resolved circulation. The passive water vapor is thermodynamically perturbed by modifying the saturation specific humidity used in the calculation of its condensation tendency and surface evaporation. The difference between the precipitation of the perturbed passive water vapor relative to the control one corresponds to the thermodynamic component of precipitation change, which can be evaluated for the entire distribution. Here, we evaluate wet and dry extremes. Our simulations have tropical increases and higher latitude decreases of dry spells’ length (defined as the maximum consecutive dry days), as found in the zonal-mean of comprehensive models. This simulated thermodynamically induced intensification of dry spells in the tropics arises from the decreased contrast between sea surface temperature and surface air temperature with warming. There is a simulated increase in heavy daily rainfall (e.g., the 99.9th percentile of the daily precipitation distribution) at all latitudes that differs modestly from a previous theory that assumes moist adiabatic stratification. Consistent with this theory, increased warming aloft slightly damps the simulated increase.

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Jing Duan
,
Yuanlong Li
,
Lijing Cheng
,
Pengfei Lin
, and
Fan Wang

Abstract

The heat content in the Indian Ocean has been increasing owing to anthropogenic greenhouse warming. Yet, where and how the anthropogenic heat is stored in the Indian Ocean have not been comprehended. Analysis of various observational and model-based datasets since the 1950s reveals a robust spatial pattern of the 0-700 m ocean heat content trend (ΔOHC), with enhanced warming in the subtropical southern Indian Ocean (SIO) but weak to minimal warming in the tropical Indian Ocean (TIO). The meridional temperature gradient between the TIO and SIO declined by 16.4%±7.5% during 1958-2014. The heat redistribution driven by time-varying surface winds plays a crucial role in shaping this ΔOHC pattern. Sensitivity experiments using a simplified ocean dynamical model suggest that changes in surface winds over the Indian Ocean, particularly those of the SIO, caused a convergence trend in the upper SIO and a divergence trend in the upper TIO. These wind changes primarily include the enhancements of westerlies in the Southern Ocean and the subtropical anticyclone in the SIO. Albeit with systematic biases, the ΔOHC pattern and surface wind changes simulated by Coupled Model Intercomparison Project Phase-6 (CMIP6) models broadly resemble the observation and highlight the essence of external forcing in causing these changes. This heat storage pattern is projected to persist in the model-projected future, potentially impacting future climate.

Restricted access
Shabana Kamal
and
Ilan Noy

Abstract

The interaction between climate change, agriculture, and financial markets is a topic that has been researched relatively little thus far. This paper intends to extend the literature by empirically testing the relationships between droughts and farms’ financing choices (measured in terms of real debt and equity) in New Zealand. Using microeconomic farm-level financial records available from the tax authorities, we quantify how past droughts (measured by the New Zealand Pasture Growth Index) impact farms' financing choices. We show a statistically significant positive impact of droughts on short-term and long-term debts, equity for dairy farms, and short-term debt for sheep and beef farms.

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Rebecca D. Adams-Selin
,
Christina Kalb
,
Tara Jensen
,
John Henderson
,
Tim Supinie
,
Lucas Harris
,
Yunheng Wang
,
Burkely T. Gallo
, and
Adam J. Clark

Abstract

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast.

The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods.

Restricted access
Yunwei Yan
,
Xiangzhou Song
, and
Marilena Oltmanns

Abstract

High-frequency observations of surface winds over the open ocean are available only at limited locations. However, these observations are essential for assessing atmospheric influences on the ocean, validating reanalysis products, and building parameterization schemes. By analyzing high-frequency measurements from the Global Tropical Moored Buoy Array, the effects of subdaily winds on the mean surface wind stress magnitude are systematically examined. Subdaily winds account for 12.4% of the total stress magnitude on average. The contribution is enhanced over the Intertropical Convergence Zone and reaches a maximum (28.5%) in the equatorial western Pacific. The magnitude of the contribution is primarily determined by the kinetic energy of subdaily winds. Compared to the buoy observations, the ERA5 and MERRA2 subdaily winds underestimate this contribution by 51% and 63% due to underestimations of subdaily kinetic energy, leading to 7% and 8% underestimations in the total stress magnitude, respectively. Two new gustiness parameterization schemes related to precipitation are developed to account for the effect of subdaily winds, explaining ~80% of the contribution from subdaily winds. Considering the importance of wind stress for ocean-atmosphere interactions, the inclusion of these parametrization schemes in climate models is expected to substantially improve simulations of large-scale climate variability.

Restricted access
Mina Masoud
and
Rich Pawlowicz

Abstract

The Strait of Georgia is a large and deep fjordlike basin on the northeastern Pacific coast whose bottom waters are dramatically renewed by a series of intermittent gravity currents in summer. Here, we analyze a dataset that includes moored observations from 2008 to 2021 and shipborne measurements from a 2018 field program to describe the vertical and cross-channel structure of these gravity currents. We show that the timing of these currents for more than a decade is well predicted by proxy measurements for both tidal mixing strength in the Haro Strait/Boundary Pass region and coastal upwelling on the west coast of Vancouver Island. Renewals occur as an ∼30-m-thick turbid layer extending along the right-hand slope of a broad V-shaped valley that forms the southern end of the strait. Currents are primarily along-isobath at speeds of up to 20 cm s−1 with a small downhill component. A diagnostic analytical model with a depth-dependent eddy viscosity is fitted to the observations and confirms a clockwise rotation of current vectors with height, partly driven by boundary layer dynamics over a scale of a few meters and partly driven by Coriolis forces in the near-bottom linear density gradient. Bottom drag and (small) entrainment parameters are similar to those found in other oceanic situations, and the current is “laminar” with respect to large-scale instabilities (with Froude number ≈1 and Ekman number ≈0.01), although subject to turbulence at small scales (Reynolds number of ∼106). The predictability and reliability of this accessible rotationally modified gravity current suggests that it is an ideal geophysical laboratory for future studies of such features.

Restricted access
Lander Ver Hoef
,
Henry Adams
,
Emily J. King
, and
Imme Ebert-Uphoff

Abstract

Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the Sugar, Fish, Flower, and Gravel Dataset produced for the study of mesocale organization of clouds by Rasp et. al. in 2020. We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find, but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that a reader of this paper will leave with a better understanding of TDA and persistent homology, be able to identify problems and datasets of their own for which persistent homology could be helpful, and gain an understanding of results they obtain from applying the included GitHub example code.

Free access