The relationship between precipitation deficits and extreme hot temperatures has been documented in observation and modeling studies. However, it is unclear whether and how increases in maximum temperatures will impact diurnal temperature range (DTR) extremes. Here, we used observational data sets from meteorological stations in China to examine the trends in high DTR extremes from 1971 to 2013, represented by the percentage of high DTR days (%HDD) and maximum high DTR duration (MHDD), as well as their relationships with precipitation deficits over the past four decades in China. We identified both positive and negative trends in the %HDD and MHDD in China during each season, implying an inhomogeneous behavior of DTR and DTR extremes. Furthermore, we observed a significant negative relationship between precipitation deficits and the %HDD and MHDD during each season and the relationship was strongest in the summer. The statistical analysis of this coupled behavior indicated that precipitation deficits were related to an increase in high DTR extremes, with a 22% average higher probability of the occurrence of DTR extremes after dry conditions than wet conditions in the summer. Knowledge from this study has important implications for interpreting climate anomalies.

/pdf/precipitation-deficits-increase-high-diurnal-temperature-1on5b7fhzh.pdf

Precipitation deficits increase high diurnal temperature range extremes.

Abstract. The aerosol fine-mode fraction (FMF) is valuable for
discriminating natural aerosols from anthropogenic ones. However, most
current satellite-based FMF products are highly unreliable over land. Here,
we developed a new satellite-based global land daily FMF dataset (Phy-DL
FMF) by synergizing the advantages of physical and deep learning methods at
a 1∘ spatial resolution covering the period from 2001 to 2020. The
Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET)
measurements, based on the analysis of 361 089 data samples from 1170
AERONET sites around the world. Overall, Phy-DL FMF showed a
root-mean-square error (RMSE) of 0.136 and correlation coefficient of 0.68,
and the proportion of results that fell within the ±20 % expected
error (EE) envelopes was 79.15 %. Moreover, the out-of-site validation
from the Surface Radiation Budget (SURFRAD) observations revealed that the
RMSE of Phy-DL FMF is 0.144 (72.50 % of the results fell within the ±20 % EE). Phy-DL FMF showed superior performance over alternative deep
learning or physical approaches (such as the spectral deconvolution
algorithm presented in our previous studies), particularly for forests,
grasslands, croplands, and urban and barren land types. As a long-term
dataset, Phy-DL FMF is able to show an overall significant decreasing trend
(at a 95 % significance level) over global land areas. Based on the trend
analysis of Phy-DL FMF for different countries, the upward trend in the FMFs
was particularly strong over India and the western USA. Overall, this study
provides a new FMF dataset for global land areas that can help improve our
understanding of spatiotemporal fine-mode and coarse-mode aerosol changes. The
datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617
(Yan, 2021).


/pdf/a-global-land-aerosol-fine-mode-fraction-dataset-2001-2020-itjbdjkc.pdf

A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

Unexpected deep mixing layer in the Sichuan Basin, China

. The aerosol fine-mode fraction (FMF) is potentially valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361,089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the ±20 % expected error window was 79.15 %. Phy-DL FMF showed superior performance over alternate deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95 % significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine- and coarse-mode aerosol changes. The datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617 (Yan, 2021).

/pdf/a-global-land-aerosol-fine-mode-fraction-dataset-2001-2020-1mw3duf54j.pdf

A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

Abstract. The aerosol fine-mode fraction (FMF) is valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable over land. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physicalÂ and deep learning methods at a 1â spatial resolution covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361â089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error (RMSE) of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the Â±20â% expected error (EE) envelopes was 79.15â%. Moreover, the out-of-site validation from the Surface Radiation Budget (SURFRAD) observations revealed that the RMSE of Phy-DL FMF is 0.144 (72.50â% of the results fell within the Â±20â% EE). Phy-DL FMF showed superior performance over alternative deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95â% significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine-mode and coarse-mode aerosol changes. The datasets can be downloaded from <a href="https://doi.org/10.5281/zenodo.5105617">https://doi.org/10.5281/zenodo.5105617</a> (Yan, 2021).

Reply on RC1

The spatial and temporal variations of aerosols and their relationships with meteorological conditions in Southwest China were studied to understand the aerosol-meteorology interactions The spatial distribution of aerosols shows a strong “basin” effect with the highest values of Aerosol Optical Thickness at 055 µm (AOT, also denoted τ055) (>070), fine-mode AOT (τ055-fine) (040 ~ 067), and coarse-mode AOT (τ055-coarse) (040 ~ 054), all observed in the Sichuan Basin The temporal variation of seasonal mean τ055 shows dissimilar cyclic variations, which is due to the contrary annual cycle trends of seasonal mean τ055-coarse (first decrease and then increase) and τ055-fine (first increase and then decrease) Both the results obtained from self-organizing map (SOM) model and principal component analysis (PCA) could imply the reasons for different features of the spatial and temporal variations of τ055, τ055-fine, and τ055-coarse, and also confirm that their distributions are influenced by complex interactions among different meteorological factors The seasonal mean τ055 during the selected period is positively correlated with air pressure (PRS) and relative humidity (RHU) but negatively correlated with sunshine duration (SSD) The seasonal mean τ055-fine is positively correlated with PRS, RHU, average temperature (TEM), and precipitation (PRE) However, the seasonal mean τ055-coarse is only negatively correlated with PRE and evaporation (EVP) In particular, the τ055-fine increases (decreases) and τ055-coarse decreases (increases) with increasing (decreasing) TEM and PRE This discrepant trend of τ055-fine and τ055-coarse might account for the weak relationships between τ055 and these two meteorological factors

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014JD022083

Statistical properties of aerosols and meteorological factors in Southwest China

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Zuwu Wang

Author Tools

Chat about Author

Papers

Statistical properties of aerosols and meteorological factors in Southwest China