Browse

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
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
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
Chia-Wei Lan
,
Chao-An Chen
, and
Min-Hui Lo

Abstract

Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as South-Eastern China, Maritime Continent, Eastern Europe, and Eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth-generation ECMWF atmospheric reanalysis (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend towards increasing dry season precipitation over land. The more water vapor in low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm.

Restricted access
Ingo Richter
,
Jayanthi V Ratnam
,
Patrick Martineau
,
Pascal Oettli
,
Takeshi Doi
,
Tomomichi Ogata
,
Takahito Kataoka
, and
François Counillon

Abstract

Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical post-processing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea-surface temperature fields at 1–12 months’ lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month.

Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics.

The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent.

Restricted access
Yuanyuan Zhou
and
Liang Gao

Abstract

The spatiotemporal variations of annual tropical cyclone- and non-tropical cyclone-induced rainfall (TCR and NTCR) during 1960 – 2017 in Southeast China are investigated in this study. The teleconnections to sea surface temperature, Arctic Oscillation, Southern Oscillation, and Indian Ocean Dipole are examined. A significant decrease in annual TCR in the Pearl River Basin was detected, while an increase in annual TCR in rainstorms was observed in the northeast of the Pearl River Basin and south of the Yangtze River Basin. Northward migration of a TCR belt was identified, which was also indicated by the pronounced anomalies of annual TCR. There was in general an increasing trend of non-tropical cyclone-induced moderate rain, heavy rain, and rainstorms in Southeast China. Compared with the non-tropical cyclone-induced heavy rain, the abnormal non-tropical cyclone-induced rainstorms are more northerly. Both monthly TCR and NTCR were remarkably affected by the Arctic Oscillation, Southern Oscillation, and Indian Ocean Dipole. TCR was more easily affected by Arctic Oscillation compared to NTCR.

Restricted access
Shuaibing Shao
,
Xin-Min Zeng
,
Ning Wang
,
Irfan Ullah
, and
Haishen Lv

Abstract

Currently, there is a lack of investigating moisture sources for precipitation over the upstream catchment of the Three Gorges Dam (UCTGD), the world’s largest dam. Using the dynamical recycling model (DRM), trajectory frequency method (TFM), and the Climate Forecast System Reanalysis (CFSR), this study quantifies moisture sources and transport paths for UCTGD summer precipitation from 1980 to 2009 based on two categories of sources: region-specific and source-direction. Overall, the land and oceanic sources contribute roughly 63% and 37%, respectively, of the moisture to UCTGD summer precipitation. UCTGD and the Indian Ocean are the most important land and oceanic sources, respectively, in which the southern Indian Ocean with over 10% of moisture contribution was overlooked previously. Under the influence of the Asian monsoon and prevailing westerlies, the land contribution decreases to 57.3% in June, then gradually increases to 68.8%. It is found that for drought years with enhanced southwest monsoon, there is a weakening of the moisture contribution from the C-shaped belt along the Arabian Sea, South Asia, and UCTGD, and vice versa. TFM results show three main moisture transport paths and highlight the importance of moisture from the southwest. Comparison analysis indicates that, generally, sink regions are more affected by land evaporation with their locations more interior to the center of the mainland. Furthermore, correlations between moisture contributions and indices of general circulation and sea surface temperature are investigated, suggesting that these indices affect precipitation by influencing moisture contributions of the subregions. All of these are useful for comprehending the causes of summer UCTGD precipitation.

Significance Statement

Quantitative research on the moisture sources of summer precipitation has been implemented for the upstream catchment of the Three Gorges Dam (UCTGD), which is of particular hydrological significance but has not been investigated previously. The dynamical recycling model (DRM)–trajectory frequency method (TFM) approach is used to quantify and interpret the results of the moisture sources both in different specific subregions and directions, which produce more meaningful results than a single method for the areal division of moisture sources. Furthermore, antecedent indices that significantly influence the following moisture contributions of the subregions and then summer UCTGD precipitation are studied in terms of large-scale general circulation indices, which would help our understanding of precipitation forecast for UCTGD.

Restricted access
Cyrille Flamant
,
Jean-Pierre Chaboureau
,
Julien Delanoë
,
Marco Gaetani
,
Cédric Jamet
,
Christophe Lavaysse
,
Olivier Bock
,
Maurus Borne
,
Quitterie Cazenave
,
Pierre Coutris
,
Juan Cuesta
,
Laurent Menut
,
Clémantyne Aubry
,
Angela Benedetti
,
Pierre Bosser
,
Sophie Bounissou
,
Christophe Caudoux
,
Hélène Collomb
,
Thomas Donal
,
Guy Febvre
,
Thorsten Fehr
,
Andreas H. Fink
,
Paola Formenti
,
Nicolau Gomes Araujo
,
Peter Knippertz
,
Eric Lecuyer
,
Mateus Neves Andrade
,
Cédric Gacial Ngoungué Langué
,
Tanguy Jonville
,
Alfons Schwarzenboeck
, and
Azusa Takeishi

Abstract

During the boreal summer, mesoscale convective systems generated over West Africa propagate westward and interact with African easterly waves, and dust plumes transported from the Sahel and Sahara by the African easterly jet. Once off West Africa, the vortices in the wake of these mesoscale convective systems evolve in a complex environment sometimes leading to the development of tropical storms and hurricanes, especially in September when sea surface temperatures are high. Numerical weather predictions of cyclogenesis downstream of West Africa remains a key challenge due to the incomplete understanding of the clouds–atmospheric dynamics–dust interactions that limit predictability. The primary objective of the Clouds–Atmospheric Dynamics–Dust Interactions in West Africa (CADDIWA) project is to improve our understanding of the relative contributions of the direct, semidirect, and indirect radiative effects of dust on the dynamics of tropical waves as well as the intensification of vortices in the wake of offshore mesoscale convective systems and their evolution into tropical storms over the North Atlantic. Airborne observations relevant to the assessment of such interactions (active remote sensing, in situ microphysics probes, among others) were made from 8 to 21 September 2021 in the tropical environment of Sal Island, Cape Verde. The environments of several tropical cyclones, including Tropical Storm Rose, were monitored and probed. The airborne measurements also serve the purpose of regional model evaluation and the validation of spaceborne wind, aerosol and cloud products pertaining to satellite missions of the European Space Agency and EUMETSAT (including the Aeolus, EarthCARE, and IASI missions).

Open access