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Amanda Richter
and
Timothy J. Lang

Abstract

NASA’s Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign gathered data using “satellite-simulating” (albeit with higher-resolution data than satellites currently provide) and in situ aircraft to study snowstorms, with an emphasis on banding. This study used three IMPACTS microwave instruments—two passive and one active—chosen for their sensitivity to precipitation microphysics. The 10–37-GHz passive frequencies were well suited for detecting light precipitation and differentiating rain intensities over water. The 85–183-GHz frequencies were more sensitive to cloud ice, with higher cloud tops manifesting as lower brightness temperatures, but this did not necessarily correspond well to near-surface precipitation. Over land, retrieving precipitation information from radiometer data is more difficult, requiring increased reliance on radar to assess storm structure. A dual-frequency ratio (DFR) derived from the radar’s Ku- and Ka-band frequencies provided greater insight into storm microphysics than reflectivity alone. Areas likely to contain mixed-phase precipitation (often the melting layer/bright band) generally had the highest DFR, and high-altitude regions likely to contain ice usually had the lowest DFR. The DFR of rain columns increased toward the ground, and snowbands appeared as high-DFR anomalies.

Significance Statement

Winter precipitation was studied using three airborne microwave sensors. Two were passive radiometers covering a broad range of frequencies, while the other was a two-frequency radar. The radiometers did a good job of characterizing the horizontal structure of winter storms when they were over water, but struggled to provide detailed information about winter storms when they were over land. The radar was able to provide vertically resolved details of storm structure over land or water, but only provided information at nadir, so horizontal structure was less well described. The combined use of all three instruments compensated for individual deficiencies, and was very effective at characterizing overall winter storm structure.

Open access
Katrina S. Virts
,
Timothy J. Lang
,
Dennis E. Buechler
, and
Phillip M. Bitzer

Abstract

Identical Lightning Imaging Sensors aboard the Tropical Rainfall Measuring Mission satellite (TRMM LIS, 1998–2015) and International Space Station (ISS LIS, 2017–present) have provided over two decades of lightning observations over the global tropics, with ISS LIS extending coverage into the mid-latitudes. Quantifying the detection performance of both LIS sensors is a necessary step toward generating a combined LIS climatological record and accurately combining LIS data with lightning detections from other sensors and networks. We compare lightning observations from both LIS sensors with reference sources including the Geostationary Lightning Mapper (GLM) and ground-based Earth Networks Total Lightning Network (ENTLN), Earth Networks Global Lightning Network (ENGLN), National Lightning Detection Network (NLDN), and Global Lightning Dataset (GLD360). Instead of a relative detection efficiency (DE) approach that assumes perfect performance of the reference sensor, we employ a Bayesian approach to estimate the upper limit of the absolute DE (ADE) of each system being analyzed. The results of this analysis illustrate the geographical pattern of ADE as well as its diurnal cycle and yearly evolution. Reference network ADE increased by ~15–30% during the TRMM era, leading to a decline in TRMM LIS ADE. ISS LIS flash ADE has been relatively consistent at 61–65%, about 4–5% lower than TRMM LIS at the end of its lifetime.

Restricted access
Benjamin M. Kiel
and
Brian A. Colle

Abstract

Several clustering approaches are evaluated for 1–9-day forecasts using a multimodel ensemble that includes the GEFS, ECMWF, and Canadian ensembles. Six clustering algorithms and three clustering spaces are evaluated using mean sea level pressure (MSLP) and 12-h accumulated precipitation (APCP) for cool-season extratropical cyclones across the Northeast United States. Using the MSLP cluster membership to obtain the APCP clusters is also evaluated, along with applying clustering determined at one lead time to cluster forecasts at a different lead time. Five scenarios from each clustering algorithm are evaluated using displacement and intensity/amount errors from the scenario nearest to the MSLP and 12-h APCP analyses in the NCEP GFS and ERA5, respectively. Most clustering strategies yield similar improvements over the full ensemble mean and are similar in probabilistic skill except that 1) intensity displacement space gives lower MSLP displacement and intensity errors; and 2) Euclidean space and agglomerative hierarchical clustering, when using either full or average linkage, struggle to produce reasonably sized clusters. Applying clusters derived from MSLP to 12-h APCP forecasts is not as skillful as clustering by 12-h APCP directly, especially if several members contain little precipitation. Use of the same cluster membership for one lead time to cluster the forecast at another lead time is less skillful than clustering independently at each forecast lead time. Finally, the number of members within each cluster does not necessarily correspond with the best forecast, especially at the longer lead times, when the probability of the smallest cluster being the best scenario was usually underestimated.

Significance Statement

Numerical weather prediction ensembles are widely used, but more postprocessing tools are necessary to help forecasters interpret and communicate the possible outcomes. This study evaluates various clustering approaches, combining a large number of model forecasts with similar attributes together into a small number of scenarios. The 1–9-day forecasts of both sea level pressure and 12-h precipitation are used to evaluate the clustering approaches for a large number of U.S. East Coast winter cyclones, which is an important forecast problem for this region.

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G. Cristina Recalde-Coronel
,
Benjamin Zaitchik
,
William Pan
,
Yifan Zhou
, and
Hamada Badr

Abstract

Hydrological predictions at sub-seasonal to seasonal (S2S) timescales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the Generalized Analog Regression Downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for three-month hydrological forecasts for the austral autumn season (March–April–May) using ensemble hindcasts for 2002-2017. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to one month lead, evapotranspiration up to 2 months lead, and soil moisture content up to three months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: the El Niño Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at one month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Open access
Zhiming Kuang

Abstract

Methods in system identification are used to obtain linear time-invariant state-space models that describe how horizontal averages of temperature and humidity of a large cumulus ensemble evolve with time under small forcing. The cumulus ensemble studied here is simulated with cloud-system-resolving models in radiative–convective equilibrium. The identified models extend steady-state linear response functions used in past studies and provide accurate descriptions of the transfer function, the noise model, and the behavior of cumulus convection when coupled with two-dimensional gravity waves. A novel procedure is developed to convert the state-space models into an interpretable form, which is used to elucidate and quantify memory in cumulus convection. The linear problem studied here serves as a useful reference point for more general efforts to obtain data-driven and interpretable parameterizations of cumulus convection.

Restricted access
Elizabeth Tirone
,
Subrata Pal
,
William A Gallus Jr.
,
Somak Dutta
,
Ranjan Maitra
,
Jennifer Newman
,
Eric Weber
, and
Israel Jirak

Abstract

Many concerns are known to exist with thunderstorm wind reports in the National Center for Environmental Information Storm Events Database, including the overestimation of wind speed, changes in report frequency due to population density, and differences in reporting due to damage tracers. These concerns are especially pronounced with reports that are not associated with a wind speed measurement, but are estimated, which make up almost 90% of the database. We have used machine learning to predict the probability that a severe wind report was caused by severe intensity wind, or wind ≥ 50 kt. A total of six machine learning models were trained on 11 years of measured thunderstorm wind reports, along with meteorological parameters, population density, and elevation. Objective skill metrics such as the area under the ROC curve (AUC), Brier score, and reliability curves suggest that the best performing model is the stacked generalized linear model, which has an AUC around 0.9 and a Brier score around 0.1. The outputs from these models have many potential uses such as forecast verification and quality control for implementation in forecast tools. Our tool was evaluated favorably at the Hazardous Weather Testbed Spring Forecasting Experiments in 2020, 2021, and 2022.

Open access
Free access
Free access
Todd Emmenegger
,
Fiaz Ahmed
,
Yi-Hung Kuo
,
Shaocheng Xie
,
Chengzhu Zhang
,
Cheng Tao
, and
J. David Neelin

Abstract

Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe , and saturation equivalent potential temperature, θes , in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe /θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

Open access
Bei Xu
and
Gen Li

Abstract

The hydrological effect of snow over Eurasia is important for regulating regional and global climate through affecting land–atmosphere energy exchange. Based on observational and reanalysis datasets, this study investigates the effect of spring Eurasian snowmelt on the following summer rainfall over eastern China during the period of 1979–2018. The results show that a substantial meridional dipole pattern of summer rainfall anomalies over eastern China is closely associated with the preceding spring snowmelt anomalies over Eurasia, especially over remote Siberia. Excessive snowmelt anomalies over Siberia in spring could result in a wetter local soil condition from spring until the following summer, thereby increasing latent heat fluxes and reducing local surface temperature, and vice versa. Then, the anomalous summer surface cooling over Siberia increases the meridional gradient of temperature between the Eurasian midlatitudes and high latitudes, which intensifies the Eurasian atmospheric baroclinicity and motivates the eddy‐induced geopotential height responses along with the significant wave propagations spreading from the Eurasian high latitudes to Lake Baikal. As a result, excessive spring snowmelt anomalies over Siberia tend to be accompanied with an anomalous anticyclone circulation to the east of Lake Baikal and an anomalous cyclonic circulation over southeastern China in the following summer. This could lead to a meridional dipole pattern of summer rainfall anomalies over eastern China, with deficient rainfall over northern China and slightly excessive rainfall over southern China. The present findings highlight the lagged effect of spring Eurasian snowmelt on summer climate over eastern China, with implications for the regional seasonal climate prediction.

Significance Statement

Frequent summer droughts and floods over eastern China cause serious damage to the regional economy and society. This study reveals an important effect of spring Eurasian snowmelt anomalies on the following summer rainfall anomalies over eastern China. Through affecting the local land–atmosphere energy exchange and regulating the remote atmospheric circulation, excessive spring Eurasian snowmelt anomalies could induce a substantial meridional dipole pattern of summer rainfall anomalies over eastern China, with deficient rainfall over northern China and excessive rainfall over southern China, and vice versa. Therefore, this work identifies spring Eurasian snowmelt as a potential seasonal predictor for summer rainfall over eastern China, which is home to more than a billion people. This is important for the local livelihood, including agriculture, water resources, ecosystems, human health, and economies.

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