Next Article in Journal
Identification of Ecological Sources Using Ecosystem Service Value and Vegetation Productivity Indicators: A Case Study of the Three-River Headwaters Region, Qinghai–Tibetan Plateau, China
Next Article in Special Issue
A Novel Method for Cloud and Cloud Shadow Detection Based on the Maximum and Minimum Values of Sentinel-2 Time Series Images
Previous Article in Journal
Advanced Soil Organic Matter Prediction with a Regional Soil NIR Spectral Library Using Long Short-Term Memory–Convolutional Neural Networks: A Case Study
Previous Article in Special Issue
Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Kueh, M.-T.; Lin, C.-Y. Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition. Remote Sens. 2022, 14, 4077

Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(7), 1257; https://doi.org/10.3390/rs16071257
Submission received: 7 February 2024 / Accepted: 18 February 2024 / Published: 2 April 2024
(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)
Figure Legend
In the original publication [1], there was a mistake in the legend for Figure 5b,c and Figure 9. The errors arose when the authors made necessary text style modifications to the figures during the proofreading stage. The correct legend appears below. The authors confirm that the legends in Figure 5 and Figure 9 were verified and found to be accurate during the review process. The authors also can confirm that these corrections do not impact the corresponding figure captions and the scientific conclusions drawn in the article. This correction was approved by the Academic Editor. The original publication has also been updated.
Correct Figure 5:
Figure 5. The seasonal variation of the joint probability distribution of the COT binned against CTP for liquid water clouds over the ICP. The seasonal variation is revealed by the statistics of four 15-day periods from 16 April to 14 June in the years from 2003 to 2021. From top to bottom panels, the 15-day periods are 16 April to 30 April, 1 May to 15 May, 15 May to 30 May, and 31 May to 14 June, respectively. (a) The normalized joint probability is displayed as two-dimensional histograms, with COT as the x-axis and CTP as the y-axis. The number in square brackets is the total count for each statistical period. For every bin box, the joint probability value is equal to the bin count divided by the total count (relative frequency). The normalized joint probability in each bin box is computed by dividing the corresponding joint probability value by the bin size (the area of that particular bin box), thereby obtaining the probability density value of the bin box. This normalization is intended to take into account the bin sizes so as to eliminate any visual distortions when comparing bins of different sizes in the two-dimensional histogram plot. The probability of any pixel that falls in a joint histogram bin box can be retrieved after multiplying the corresponding probability density value by that bin size. The bin colors represent the probability density values in each bin. The COT histogram bins ranging from 30 to 150 were chopped off for plotting purposes, owing to the large bin range and very low probability density. The marginal probabilities of CTP with respect to three COT categories are displayed as (b) stacked and (c) overlapping histograms, with probability as the x-axis and CTP as the y-axis. The marginal probabilities of CTP are derived from the joint histograms of COT binned against CTP. The COT is grouped into three categories of newly aggregated bins from the original COT histogram bins: 0–4, 4–20, 20–150. Three sets of the marginal probability of CTP are obtained by integrating (summing) the joint probability over the specific ranges of the aggregated bins from the three COT categories, respectively.
Figure 5. The seasonal variation of the joint probability distribution of the COT binned against CTP for liquid water clouds over the ICP. The seasonal variation is revealed by the statistics of four 15-day periods from 16 April to 14 June in the years from 2003 to 2021. From top to bottom panels, the 15-day periods are 16 April to 30 April, 1 May to 15 May, 15 May to 30 May, and 31 May to 14 June, respectively. (a) The normalized joint probability is displayed as two-dimensional histograms, with COT as the x-axis and CTP as the y-axis. The number in square brackets is the total count for each statistical period. For every bin box, the joint probability value is equal to the bin count divided by the total count (relative frequency). The normalized joint probability in each bin box is computed by dividing the corresponding joint probability value by the bin size (the area of that particular bin box), thereby obtaining the probability density value of the bin box. This normalization is intended to take into account the bin sizes so as to eliminate any visual distortions when comparing bins of different sizes in the two-dimensional histogram plot. The probability of any pixel that falls in a joint histogram bin box can be retrieved after multiplying the corresponding probability density value by that bin size. The bin colors represent the probability density values in each bin. The COT histogram bins ranging from 30 to 150 were chopped off for plotting purposes, owing to the large bin range and very low probability density. The marginal probabilities of CTP with respect to three COT categories are displayed as (b) stacked and (c) overlapping histograms, with probability as the x-axis and CTP as the y-axis. The marginal probabilities of CTP are derived from the joint histograms of COT binned against CTP. The COT is grouped into three categories of newly aggregated bins from the original COT histogram bins: 0–4, 4–20, 20–150. Three sets of the marginal probability of CTP are obtained by integrating (summing) the joint probability over the specific ranges of the aggregated bins from the three COT categories, respectively.
Remotesensing 16 01257 g005
Correct Figure 9:
Figure 9. The marginal probabilities of CTP with respect to three categories of COT compiled over the (a) BoB, (b) ICP, and (c) SCS regions, collected from May of cold (blue tone) and warm (red tone) years. The BoB, ICP, and SCS regions are indicated in Figure 8b. The COT is for liquid water cloud in the daytime and is a dimensionless quantity, CTP is in hPa. The numbers in square brackets are the total counts for these statistical periods. The marginal probabilities of CTP are derived from the joint histograms of COT binned against CTP and are displayed as stacked histograms with probability as the x-axis and CTP as the y-axis. The COT is grouped into three categories of newly aggregated bins from the original COT histogram bins: 0–4, 4–20, and 20–150, whereas the CTP are grouped into three categories of newly aggregated bins from the original CTP histogram bins: 50–450, 440–700, and 700–1100 hPa. For each plot panel, three sets of the marginal probabilities of CTP are obtained by integrating the joint probability (relative frequency) over the specific ranges of the aggregated bins from the three corresponding COT categories, respectively.
Figure 9. The marginal probabilities of CTP with respect to three categories of COT compiled over the (a) BoB, (b) ICP, and (c) SCS regions, collected from May of cold (blue tone) and warm (red tone) years. The BoB, ICP, and SCS regions are indicated in Figure 8b. The COT is for liquid water cloud in the daytime and is a dimensionless quantity, CTP is in hPa. The numbers in square brackets are the total counts for these statistical periods. The marginal probabilities of CTP are derived from the joint histograms of COT binned against CTP and are displayed as stacked histograms with probability as the x-axis and CTP as the y-axis. The COT is grouped into three categories of newly aggregated bins from the original COT histogram bins: 0–4, 4–20, and 20–150, whereas the CTP are grouped into three categories of newly aggregated bins from the original CTP histogram bins: 50–450, 440–700, and 700–1100 hPa. For each plot panel, three sets of the marginal probabilities of CTP are obtained by integrating the joint probability (relative frequency) over the specific ranges of the aggregated bins from the three corresponding COT categories, respectively.
Remotesensing 16 01257 g009

Reference

  1. Kueh, M.-T.; Lin, C.-Y. Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition. Remote Sens. 2022, 14, 4077. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kueh, M.-T.; Lin, C.-Y. Correction: Kueh, M.-T.; Lin, C.-Y. Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition. Remote Sens. 2022, 14, 4077. Remote Sens. 2024, 16, 1257. https://doi.org/10.3390/rs16071257

AMA Style

Kueh M-T, Lin C-Y. Correction: Kueh, M.-T.; Lin, C.-Y. Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition. Remote Sens. 2022, 14, 4077. Remote Sensing. 2024; 16(7):1257. https://doi.org/10.3390/rs16071257

Chicago/Turabian Style

Kueh, Mien-Tze, and Chuan-Yao Lin. 2024. "Correction: Kueh, M.-T.; Lin, C.-Y. Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition. Remote Sens. 2022, 14, 4077" Remote Sensing 16, no. 7: 1257. https://doi.org/10.3390/rs16071257

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop