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Thomas C. Pagano
,
Barbara Casati
,
Stephanie Landman
,
Nicholas Loveday
,
Robert Taggart
,
Elizabeth E. Ebert
,
Mohammadreza Khanarmuei
,
Tara L. Jensen
,
Marion Mittermaier
,
Helen Roberts
,
Steve Willington
,
Nigel Roberts
,
Mike Sowko
,
Gordon Strassberg
,
Charles Kluepfel
,
Timothy A. Bullock
,
David D. Turner
,
Florian Pappenberger
,
Neal Osborne
, and
Chris Noble

Abstract

Operational agencies face significant challenges related to the verification and evaluation of weather forecasts. These challenges were investigated in a series of online workshops and polls engaging operational personnel from six countries. Five key themes emerged: inadequate verification approaches for both existing and emerging products; incomplete and uncertain observations; difficulties in accurately capturing users’ real-world experiences using simplified metrics; poor communication and understanding of forecasts and complex verification information; and institutional factors such as limited resources, evolving meteorologist roles, and concerns over reputational damage. We identify nearly 50 operationally relevant scientific questions and suggest calls to action. Addressing these needs includes designing forecast systems with verification as a central consideration, enhancing the availability of observations, and developing and adopting community software systems. Additionally, we propose the establishment of an international community comprising environmental and social science researchers, statisticians, verification practitioners, and users to provide sustained support for this collective endeavor.

Open access
Eun-Pa Lim
,
Harry H. Hendon
,
Amy H. Butler
,
David W. J. Thompson
,
Zachary D. Lawrence
,
Adam A. Scaife
,
Theodore G. Shepherd
,
Inna Polichtchouk
,
Hisashi Nakamura
,
Chiaki Kobayashi
,
Ruth Comer
,
Lawrence Coy
,
Andrew Dowdy
,
Rene D. Garreaud
,
Paul A. Newman
, and
Guomin Wang
Open access
Gabor Vali
and
Russell C. Schnell

Abstract

An overview is given of the path of research that led from asking how hailstones originate to the discovery that ice nucleation can be initiated by bacteria and other microorganisms at temperatures as high as −2°C. The major steps along that path were finding exceptionally effective ice nucleators in soils with a high content of decayed vegetative matter, then in decaying tree leaves, and then in plankton-laden ocean water. Eventually, it was shown that Pseudomonas syringae bacteria were responsible for most of the observed activity. That identification coincided with the demonstration that the same bacteria cause frost damage on plants. Ice nucleation by bacteria meant an unexpected turn in the understanding of ice nucleation and of ice formation in the atmosphere. Subsequent research confirmed the unique effectiveness of ice nucleating particles (INP) of biological origin, referred to as bio-INPs, so that bio-INPs are now considered to be important elements of lower-tropospheric cloud processes. Nonetheless, some of the questions which originally motivated the research are still unresolved, so that revisiting the early work may be helpful to current endeavors. Part I of this manuscript summarizes how the discovery progressed. Part II (Schnell and Vali) shows the relationship between bio-INPs in soils and in precipitation with climate and other findings. The online supplemental material contains a bibliography of recent work about bio-INPs.

Open access
Ming Cai
,
Xiaoming Hu
,
Jie Sun
,
Feng Ding
, and
Jing Feng

Abstract

This paper introduces a climate feedback kernel, referred to as the “energy gain kernel” (EGK). EGK allows for separating the net longwave radiative energy perturbations given by a Planck feedback matrix explicitly into thermal energy emission perturbations of individual layers, and thermal radiative energy flux convergence perturbations at individual layers resulting from the coupled atmosphere-surface temperature changes in response to the unit forcing in individual layers. The former is represented by the diagonal matrix of a Planck feedback matrix and the latter by EGK. Elements of EGK are all positive, representing amplified energy perturbations at a layer where forcing is imposed and energy gained at other layers, both of which are achieved through radiative thermal coupling within an atmosphere-surface column.

Applying EGK to input energy perturbations, whether external or internal due to responses of non-temperature feedback processes to external energy perturbations, such as water vapor and albedo feedbacks, yields their total energy perturbations amplified through radiative thermal coupling within an atmosphere-surface column.

As the strength of EGK depends exclusively on climate mean states, it offers a solution for effectively and objectively separating control climate state information from climate perturbations for climate feedback studies. Given that an EGK comprises critical climate mean state information on mean temperature, water vapor, clouds, and surface pressure, we envision that the diversity of EGK across different climate models could provide insight into the inquiry of why, under the same anthropogenic greenhouse gas increase scenario, different models yield varying degrees of global mean surface warming.

Open access
Guangming Zheng
,
Stephanie Schollaert Uz
,
Pierre St-Laurent
,
Marjorie A. M. Friedrichs
,
Amita Mehta
, and
Paul M. DiGiacomo

Abstract

Seasonal hypoxia is a recurring threat to ecosystems and fisheries in the Chesapeake Bay. Hypoxia forecasting based on coupled hydrodynamic and biogeochemical models has proven useful for many stakeholders, as these models excel in accounting for the effects of physical forcing on oxygen supply, but may fall short in replicating the more complex biogeochemical processes that govern oxygen consumption. Satellite-derived reflectances could be used to indicate the presence of surface organic matter over the Bay. However, teasing apart the contribution of atmospheric and aquatic constituents from the signal received by the satellite is not straightforward. As a result, it is difficult to derive surface concentrations of organic matter from satellite data in a robust fashion. A potential solution to this complexity is to use deep learning to build end-to-end applications that do not require precise accounting of the satellite signal from atmosphere or water, phytoplankton blooms, or sediment plumes. By training a deep neural network with data from a vast suite of variables that could potentially affect oxygen in the water column, improvement of short-term (daily) hypoxia forecast may be possible. Here we predict oxygen concentrations using inputs that account for both physical and biogeochemical factors. The physical inputs include wind velocity reanalysis information, together with 3D outputs from an estuarine hydrodynamic model, including current velocity, water temperature, and salinity. Satellite-derived spectral reflectance data are used as a surrogate for the biogeochemical factors. These input fields are time series of weekly statistics calculated from daily information, starting 8 weeks before each oxygen observation was collected. To accommodate this input data structure, we adopted a model architecture of long short-term memory networks with 8 time steps. At each time step, a set of convolutional neural networks are used to extract information from the inputs. Ablation and cross validation tests suggest that among all input features, the strongest predictor is the 3D temperature field, with which the new model can outperform the state-of-the-art by ∼20% in terms of median absolute error. Our approach represents a novel application of deep learning to address a complex water management challenge.

Open access
Erik S Krueger
,
Tyson E Ochsner
, and
B Wade Brorsen

Abstract

The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the United States Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic drought. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to (1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, (2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and (3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, USA, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

Open access
Daniel Galea
,
Kevin Hodges
, and
Bryan N. Lawrence

Abstract

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.

Open access
S. Kalluri
,
C. Cao
,
A. Heidinger
,
A. Ignatov
,
J. Key
, and
T. Smith
Full access
Susan C. van den Heever
,
Leah D. Grant
,
Sean W. Freeman
,
Peter J. Marinescu
,
Julie Barnum
,
Jennie Bukowski
,
Eleanor Casas
,
Aryeh J. Drager
,
Brody Fuchs
,
Gregory R. Herman
,
Stacey M. Hitchcock
,
Patrick C. Kennedy
,
Erik R. Nielsen
,
J. Minnie Park
,
Kristen Rasmussen
,
Muhammad Naufal Razin
,
Ryan Riesenberg
,
Emily Riley Dellaripa
,
Christopher J. Slocum
,
Benjamin A. Toms
, and
Adrian van den Heever
Full access
Full access