bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model
Authors: Liu Qinqing , Yang Meijian , Mohammadi Koushan , Song Dongjin , Bi Jinbo , Wang Guiling 
Published: August 2022

Abstract
A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate.

Significance Statement
Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.
The Pairwise Similarity Partitioning Algorithm: A Method for Unsupervised Partitioning of Geoscientific and Other Datasets Using Arbitrary Similarity Metrics
Authors: Petty Grant W. 
Published: August 2022

Abstract
A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, nonoverlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, although clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two nontrivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well suited for isolating rare or anomalous members of a dataset. The method is inductive in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.
What Determines Above-Anvil Cirrus Plume Infrared Temperature?
Authors: Murillo Elisa M. , Homeyer Cameron R. 
Published: August 2022

Abstract
Above-anvil cirrus plumes (AACPs) in midlatitude convection are important indicators of severe storms and stratospheric hydration events. Recent studies of AACPs have shown large variability in their characteristics, although many of the causes remain unknown. Notably, some AACPs appear equally as cold (or colder) than the broader storm top when compared to the more frequently observed warm AACP feature in infrared satellite imagery. To confidently identify the presence of an AACP, trained experts utilize infrared imagery to support the primary source of AACP identification, visible imagery. Thus, nighttime AACPs are often left unidentified due to unavailable visible imagery, especially for cold AACPs. In this study, 89 warm and 89 cold AACPs from 1-min GOES-16 satellite imagery coupled with ground-based radar observations and reanalysis data are comparatively evaluated to answer the following research questions: 1) Why do some AACPs exhibit a warm feature in infrared imagery while others do not, and 2) what observable storm and environment differences exist between warm and cold AACPs? It is found that cold AACPs tend to occur in tropical environments, which feature higher, cold-point tropopauses. Conversely, warm AACPs tend to occur in midlatitude environments, with lower tropopauses accompanied by an isothermal region (or tropopause inversion layer) in the lower stratosphere. Similar storm characteristics are found for warm and cold AACP events, implying that infrared temperature variability is driven by environmental differences. Together, these results suggest that cold AACPs are predominantly tropospheric phenomena, while warm AACPs reside in the lower stratosphere.

Significance Statement
The purpose of this study is to determine why some storms with a specific cloud-top feature exhibit a broad warm spot in infrared satellite imagery while others appear cold. This is important because storms with this specific cloud-top feature, whether warm or cold, produce much more severe weather than most other storms. These cloud-top features are also potentially indicative of increased water vapor in the stratosphere, which results in warming of Earth’s climate. Our results help us better understand storms that are frequently severe and suggest that the storms with cold features are less important to understanding stratospheric water vapor and climate change.
On the Diffusivity of Moist Static Energy and Implications for the Polar Amplification Response to Climate Warming
Authors: Lu Jian , Zhou Wenyu , Kong Hailu , Leung L. Ruby , Harrop Bryce , Song Fengfei 
Published: August 2022

Abstract
Energy balance models (EBMs) have been widely used in a range of climate problems, but the assumption of constant diffusivity in the parameterization of the moist static energy (MSE) flux can hardly be justified. We demonstrate in this study that the diffusive MSE flux can be derived from the basic energy balance equation with a few tolerable assumptions. The estimated diffusivity is both spatially and seasonally dependent, and its midlatitude average is then tested against several scaling theories for the midlatitude eddy diffusivity. The result supports the diffusivity theory of Held and Larichev modified for the moist atmosphere, affording a dynamics-based parameterization of MSE diffusivity. The implementation of the parameterization in an EBM leads to an interactive MSE diffusivity that accounts for the midlatitude eddy response to climate forcing perturbations. Under a uniform radiative forcing, the EBM with a diffusivity so parameterized produces a weakening of the midlatitude diffusivity and a modestly polar-amplified surface temperature response as an inevitable outcome under the dual constraints of the nonlinear Clausius–Clapeyron relation and the temperature gradient-dependent diffusivity, even in the absence of any poleward-amplifying radiative feedbacks. As the consequence of more isothermal temperature and reduced diffusivity, the variance of the midlatitude surface temperature also decreases with warming.
Is Anthropogenic Global Warming Accelerating?
Authors: Jenkins Stuart , Povey Adam , Gettelman Andrew , Grainger Roy , Stier Philip , Allen Myles 
Published: August 2022

Abstract
Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (from +0.4 W m−2 decade−1 in 2000–09 to +0.6 W m−2 decade−1 in 2010–19), the majority of which is driven by changes in the aerosol ERF trend, as a result of aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from the IPCC’s Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and we use aerosol optical depth observations. The GMST trend has increased from +0.18°C decade−1 in 2000–09 to +0.35°C decade−1 in 2010–19, coinciding with the anthropogenic warming trend rising from +0.19°C decade−1 in 2000–09 to +0.24°C decade−1 in 2010–19. This, as well as observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths, supports the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest that smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A zero-trend-change aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000 but is poorly represented in AR6’s ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required.
Zonal Temperature Gradients in the Tropical Free Troposphere
Authors: Bao Jiawei , Dixit Vishal , Sherwood Steven C. 
Published: August 2022

Abstract
The horizontal temperature gradients in the tropical free troposphere are generally assumed to be weak. We show with ERA5 data that substantial zonal virtual temperature (Tυ) gradients persist climatologically in the tropical free troposphere and investigate their causes. The gradients change seasonally: Tυ at 500 hPa over the equatorial western Pacific Ocean (EWP) is usually much warmer (up to 3 K) than that over the equatorial central Pacific Ocean (ECP) during December–February (DJF), while the temperature differences between EWP and ECP are much smaller during June–August (JJA). During DJF, Tυ gradients over the Pacific prevail throughout the entire free troposphere, especially in the upper troposphere near 300 hPa. We find that the associated hydrostatic pressure gradients are mainly balanced by the nonlinear terms in the momentum equation, in particular via zonal wind advection. Strong zonal winds occur near the equator in boreal winter, transporting zonal momentum so as to balance the pressure gradient force. The zonal winds are due to large-scale equatorial waves, excited by a heating pattern that is relatively symmetric about the equator. In boreal summer, the large-scale equatorial waves are less active in the Pacific region due to a more asymmetric temperature pattern, so the zonal momentum advection and Tυ gradients are both much weaker. The results point to an important role of the nonlinear terms in the tropical balanced dynamics, stressing the need for an improved theoretical understanding and modeling framework of the tropical atmosphere that includes these nonlinear terms, or their net effect.
Structures and Mechanisms of Heatwaves Related to Quasi-Biweekly Variability over Southern China
Authors: Zheng Bin , Gu Dejun , Lin Ailan , Peng Dongdong , Li Chunhui , Huang Yanyan 
Published: August 2022

Abstract
In the present study, the structures and mechanisms of the heatwaves (HWs) associated with the quasi-biweekly (QBW; 10–20-day period) variability (QBW-HW) over southern China (SC; 106°–120°E, 21°–30°N) are investigated by using observation data from surface stations in China and the related gridded dataset (CN05.1), and the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. We found that the strongest anticyclonic anomaly and subsidence appear over SC during the developing phase of QBW-HW, and then induced excess solar radiation at surface and significant diabatic heating lead to a positive surface air temperature change, thus favoring occurrence of QBW-HW over SC. In addition, we found a wet near-surface atmosphere in the QBW-HW events over SC, and further confirmed that near-surface moisture should play an important role in the occurrence of QBW-HW, via absorptions of longwave and shortwave radiation. This result is quite different from previous studies since they did not pay attention to the near-surface moisture. On the other hand, warmer SAT favors more water vapor evaporated from the moist soil when considering the Clausius–Clapeyron relationship. Then, the positive feedback processes promote the occurrence of QBW-HW over SC. In contrast, during the developing and warm phases of QBW-HW over SC, except for the near-surface level, the troposphere is in a dry condition, even at 850 and 700 hPa. In the QBW-HW events over SC, the factor responsible for the wet near-surface atmosphere is the enhanced surface evaporation, which is attributed to strengthened surface wind speed and background moist soil.

Significance Statement
Under the background of global warming, heatwaves over Southern China are experiencing an increasing trend. In this study, we want to understand the structures and mechanisms of the heatwaves related to 10–20-day (quasi-biweekly) variability. We that found some structures of heatwaves (e.g., anticyclonic anomalies along with subsidence) are consistent with previous studies. In addition, we also show that the moist soil and increased induced near-surface moisture play a key role in the occurrence of heatwaves over Southern China, via enhanced absorptions of longwave and shortwave radiation. This study is helpful for understanding the processes and prediction of heatwaves over Southern China. Future work should examine the findings by some numerical experiments with a climate model.
Blending TAC and BUFR Marine In Situ Data for ICOADS Near-Real-Time Release 3.0.2
Authors: Liu Chunying , Freeman Eric , Kent Elizabeth C. , Berry David I. , Worley Steven J. , Smith Shawn R. , Huang Boyin , Zhang Huai-min , Cram Thomas , Ji Zaihua , Ouellet Mathieu , Gaboury Isabelle , Oliva Frank , Andersson Axel , Angel William E. , Sallis Angela R. , Adeyeye Adedoja 
Published: August 2022

Abstract
This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2.

Significance Statement
The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version.
Three-Dimensional Convective–Stratiform Echo-Type Classification and Convectivity Retrieval from Radar Reflectivity
Authors: Dixon Michael , Romatschke Ulrike 
Published: August 2022

Abstract
The Echo Classification from COnvectivity (ECCO) algorithm identifies convective and stratiform types of radar echo in three dimensions. It is based on the calculation of reflectivity texture—a combination of the intensity and the heterogeneity of the radar echoes on each horizontal plane in a 3D Cartesian volume. Reflectivity texture is translated into convectivity, which is designed to be a quantitative measure of the convective nature of each 3D radar grid point. It ranges from 0 (100% stratiform) to 1 (100% convective). By thresholding convectivity, a more traditional qualitative categorization is obtained, which classifies radar echoes as convective, mixed, or stratiform. In contrast to previous algorithms, these echo-type classifications are provided on the full 3D grid of the reflectivity field. The vertically resolved classifications, in combination with temperature data, allow for subclassifications into shallow, mid-, deep, and elevated convective features, and low, mid-, and high stratiform regions—again in three dimensions. The algorithm was validated using datasets collected over the U.S. Great Plains during the PECAN field campaign. An analysis of lightning counts shows ∼90% of lightning occurring in regions classified as convective by ECCO. A statistical comparison of ECCO echo types with the well-established GPM radar precipitation-type categories show 84% (88%) of GPM stratiform (convective) echo being classified as stratiform (convective) or mixed by ECCO. ECCO was applied to radar grids for the continental United States, the United Arab Emirates, Australia, and Europe, illustrating its robustness and adaptability to different radar grid characteristics and climatic regions.
Investigation of Maximum Hail-Size Forecasting Using Bulk Microphysics Schemes
Authors: Wu Wanchen , Huang Wei , Deng Lin , Wu Chong 
Published: August 2022

Abstract
This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme.

Significance Statement
Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future.
EAKF-Based Parameter Optimization Using a Hybrid Adaptive Method
Authors: Cao Lige , Wu Xinrong , Han Guijun , Li Wei , Wu Xiaobo , Wu Haowen , Li Chaoliang , Li Yundong , Zhou Gongfu 
Published: August 2022

Abstract
To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.