Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases

Min, Ki-Hong; Chung, Kao-Shen; Lee, Ji-Won; You, Cheng-Rong; Lee, Gyuwon

doi:https://doi.org/10.5194/gmd-2022-18

Preprints

https://doi.org/10.5194/gmd-2022-18

Preprints

Submitted as: model experiment description paper

06 Apr 2022

Submitted as: model experiment description paper |

| 06 Apr 2022

Status: this preprint was under review for the journal GMD but the revision was not accepted.

Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases

Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

Abstract. Gangwon-do (GWD) has complex terrain and surface characteristics due to its location to the East Sea and the Taebaek Mountain range. This coastal location and rugged terrain can amplify snowfall mechanisms, making it challenging to accurately predict the amount and location. This study compares two methods for assimilating radar data and analyzed snowfall prediction results. The two methods compared are local ensemble transform Kalman filter (LETKF) and three-dimensional variational (3DVAR) data assimilation (DA). LETKF improved the water vapor amount and temperature using the covariance of the ensemble members, but 3DVAR improved the water vapor mixing ratio and temperature through an operator that assumed the atmosphere was saturated when reflectivity was above a certain threshold. In 2018, to understand the snowfall in GWD region and support the Pyeongchang Winter Olympic and Paralympic Games, a long-term heavy snow observation campaign was conducted. The International Collaborative Experiments for the 2018 Pyeongchang Olympic Games Projects (ICE-POP 2018) data are used to study and verify the numerical experiments. From the initial field verification using ICE-POP observation data (radiosonde), wind in LETKF was more accurately simulated than 3DVAR, but it underestimated the water vapor amount and temperature in the lower troposphere due to a lack of a water vapor and temperature observation operator. Snowfall in GWD was less simulated in LETKF, whereas snowfall of 10.0 mm or more was simulated in 3DVAR, resulting in an error of 2.62 mm lower than LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor and temperature in radar DA.

Received: 20 Jan 2022 – Discussion started: 06 Apr 2022

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Download & links

Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

Status: closed

EC1:
'Comment on gmd-2022-18', Yuefei Zeng, 22 Apr 2022

Dear authors,
It is noticed that you have archived your code on GitHub. However, GitHub is not a suitable repository for long-term archival and publishing alternatives. Also, you have to provide the version number for WRF, WRFDA and LETKF and their licenses. In case of no license, the code belongs to you and can not be used by others.
Therefore, please publish your code in one of the appropriate repositories (e.g., Zenodo) with version numbers and licenses, and reply to this comment as soon as possible with the link to the repository for the code and the corresponding DOI before the Open Discussion ends. Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code.
Yuefei Zeng

Geosci. Model Dev. Topical Editor

Citation: https://doi.org/10.5194/gmd-2022-18-EC1
- AC1: 'Reply on EC1', Ki-Hong Min, 12 May 2022
  
  We uploaded WPS, WRF, WRFDA codes used in the study at the zendo website "https://zenodo.org/record/6497443#.YnjbruhBwuU".
  The version of WPS is 4.1, and WRF, WRFDA, LETKF are 4.1.3, respectively. These codes are open source codes that do not require licence. In addition, radar data used for input data and the scripts to draw figures for all the results reported in this paper are included.
  We modified the paper as following:
  Code and data availability
  National Center for Environmental Prediction (NCEP) Final Analysis (FNL) 1°×1° data for initial and boundary conditions is available at https://rda.ucar.edu/datasets/ds083.2/. Observation data from KMA are available online (https://data.kma.go.kr) and ICE-POP data are available via https://doi.org/10.1594/ PANGAEA.918315. The WPS v4.1, and v4.1.3 of WRF, WRFDA and LETKF codes and scripts that cover data and figure processing action for all of the results reported in this paper are available at https://zenodo.org/record/6378081#.YjryRedBwuU. These codes are open source codes that do not require licencing. Model outputs are available upon request to Ji-Won Lee (leejiwon2040@knu.ac.kr).
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC1
RC1:
'Comment on gmd-2022-18', Anonymous Referee #1, 16 May 2022
Review of Manuscript # gmd-2022-18 in GMDD: “Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases” by Min et al.

General comments:

The authors examined the performance of two different radar data assimilation methods, LETKF and 3DVAR, in the two snowfall cases observed during ICE-POP 2018 field campaign. They found the two methods produced different analyses and forecasts in winds, water vapor, temperature, and snowfall. They concluded that assimilating water vapor is important in radar assimilation, which impacts precipitation forecasts significantly. Overall, the manuscript is well organized and easy to follow, and the logic is clear. However, there are several concerns that should be clarified before considering the manuscript for publication.

Specific comments:

The authors compared two very different radar DA methods. Therefore, there might be opposite results attained when tuning the parameters, e.g., number of ensemble members. It might be not suitable to tell which method is better. Especially, it seems that the authors used different radar reflectivity operators in the two methods. Please justify it. How about the results if the authors use the same observation operators for radar radial velocity and reflectivity in the two DA methods? How about the results if LETKF also assimilates water vapor indirectly not through covariance based on ensemble members?

If the reviewer understood correctly, only one forecast was produced in each snowfall case in each DA experiment. The reviewer would suggest that one forecast can be produced in each DA cycle, and then there are enough samples for the authors to conduct statistical evaluation (RMSE, FSS, etc.), which will make a solid study.

Sections 3.2, 3.3, and 3.5: Please conduct quantitative analysis and comparison, not just full of “large” “more” “similar” “underestimate” “increase” “relatively small/dry” “low” …

Lines 303-314: What spatial scale did the authors use to calculate FSS? Please examine the sensitivity of different spatial scales and precipitation thresholds.

Figures 12-15: How about the results of CTRL? Please include them for comparison.

Technical corrections:

Line 120: “component” -> “components”

Line 125: “BE” -> “background error”

Line 126: Please justify the observation errors used in this study.

Table 1: Please provide a brief description of prognostic variables.

Line 188: “compare” -> “examine”

Lines 194-195: Did the authors interpolate station data to the model grid?

Lines 195 and 201: “using” -> “by”

Line 196: “fractions skill score” -> “fractions skill score (FSS)”

Line 206: “at 3 km” means “at 3-km height” or “at the 3-km domain”

Lines 218-220: Please conduct a quantitative comparison.
Citation: https://doi.org/10.5194/gmd-2022-18-RC1
- AC2: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC2
- AC5: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC5
RC2:
'Comment on gmd-2022-18', Anonymous Referee #2, 21 May 2022

see attachment.

Citation: https://doi.org/10.5194/gmd-2022-18-RC2
- AC3: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC3
- AC6: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC6
- AC7: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC7
RC3:
'Comment on gmd-2022-18', Anonymous Referee #3, 31 May 2022
General comments:

The paper uses radar data to run two different data assimilation methods, LETKF and 3dVAR, during two snowfall events observed in ICE-POP 2018 fleld campaign. The authors compared the analysis and the forecasted data of different variables including wind, water vapor, temperature, and snowfall to show the importance of water vapor assimilation. The logic and structure of the paper are clear and easy to follow. However, several sentences need to be rewritten/reconsidered as well as some concerns which need to be considered before publication.

Specific comments:

It seems the authors run only one forecast cycle to compare two methods which cause few samples to do the verification methods. Please consider running more forecast cycles to have enough samples which make the verification as well as the results more reasonable.

The authors use two different data assimilation methods; however, in many sentences particularly in the results and summary sections, the "simulation" word was used to refer to the LETKF or 3DVAR methods. Please note that this is the wrong word referring to the assimilation method. The LETKF and 3DVAR do the "assimilation" not "simulation". Sentences like „The snowfall in GWD was less simulated in LETKF“ are logically wrong and need to be rewritten.

The word "underestimate" was used many times in sections 3 and 4 to compare two assimilation methods. Since non of these methods were considered as a reference experiment or reference data, using the words "underestimation“ or "overestimation“ for this comparison is meaning less. The words "underestimation" or "overestimation" could be used when the results are compared with reference data such as observation. Please consider rewriting these sentences.

In section 2.2.2 the radial wind and the reflectivity errors are assumed 3 ms-1 and 5 dbz respectively; however, the authors did not mention the source of these numbers.

In section 2.3 was mentioned that the precipitation up to 24.8 mm was recorded in the red box area from 00 to 12 UTC. It is an unclear sentence. The precipitation reported from an SYNOP station (which probably is the concern of the author in this sentence) would be for a specific point not for a whole specific area. The sentence could be rewritten by pointing to the minimum and maximum precipitation amount in this area as well as the location of the station which had a maximum report of precipitation. Please consider also the last paragraph in this section which has the same problem.

In section 3.1 the sentence „The snow mixing ratio is higher in LETKF“ is a general sentence that of course is not generally correct. Please consider mentioning clearly about the specific case (time/location) where the assimilated snow mixing ratio is higher than assimilated snow mixing ratio in 3DVAR.

In Fig. 8, there is no explanation for the dashed lines.

Fig. 14(d), there is a weird feature regarding the FSS score. The FSS scores for both LETKF and 3DVAR methods are very low at the first forecast hours and they increase after about 6 hours. This is not a common behavior in the FSS score of a forecast validation. It would be good if the authors recheck this case or explain a bit about such a weird behavior of the FSS score.

Technical correction:

Line 35: „cool ocean winds“ → „cold ocean winds“

Line 49: „has“ → „get“

Line 69: „only include information → „include only information“

Line88: „Further“ → „Furthermore“

Line 141: „improves“ → „calculates“. Please consider changing all other „improve “ in this paragraph and the next one to „calculate“ or „produce“

Line 206: „at 3 km“, 3km resolution? Or 3km height? Please specify it.

Line 208: The sentence „Increment in wind and hydrometeors show similar patterns, depending on the DA method“. Please consider rewriting this and the similar sentences in this section. The sentence is unclear and ambiguous.

Line 235: „,the LETKF underestimates the temperature,“ → „.there is an underestimation in the temperature derived from the LETKF method,“

Line 253: „Note the amount of change in the snow mixing ratio“, what is the point of this sentence?

Line 278: „The observed GRS radar CFAD“ → „The observed CFAD of the GRS radar“

Line 294: „In Case 1, The“ → „In case 1, the“

Line 305: „hour prediction“ → „forecast hours“ please also consider replacing the „prediction“ with „forecast“ in this and the next section.
Citation: https://doi.org/10.5194/gmd-2022-18-RC3
- AC4: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC4
- AC8: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC8

Status: closed

EC1:
'Comment on gmd-2022-18', Yuefei Zeng, 22 Apr 2022

Dear authors,
It is noticed that you have archived your code on GitHub. However, GitHub is not a suitable repository for long-term archival and publishing alternatives. Also, you have to provide the version number for WRF, WRFDA and LETKF and their licenses. In case of no license, the code belongs to you and can not be used by others.
Therefore, please publish your code in one of the appropriate repositories (e.g., Zenodo) with version numbers and licenses, and reply to this comment as soon as possible with the link to the repository for the code and the corresponding DOI before the Open Discussion ends. Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code.
Yuefei Zeng

Geosci. Model Dev. Topical Editor

Citation: https://doi.org/10.5194/gmd-2022-18-EC1
- AC1: 'Reply on EC1', Ki-Hong Min, 12 May 2022
  
  We uploaded WPS, WRF, WRFDA codes used in the study at the zendo website "https://zenodo.org/record/6497443#.YnjbruhBwuU".
  The version of WPS is 4.1, and WRF, WRFDA, LETKF are 4.1.3, respectively. These codes are open source codes that do not require licence. In addition, radar data used for input data and the scripts to draw figures for all the results reported in this paper are included.
  We modified the paper as following:
  Code and data availability
  National Center for Environmental Prediction (NCEP) Final Analysis (FNL) 1°×1° data for initial and boundary conditions is available at https://rda.ucar.edu/datasets/ds083.2/. Observation data from KMA are available online (https://data.kma.go.kr) and ICE-POP data are available via https://doi.org/10.1594/ PANGAEA.918315. The WPS v4.1, and v4.1.3 of WRF, WRFDA and LETKF codes and scripts that cover data and figure processing action for all of the results reported in this paper are available at https://zenodo.org/record/6378081#.YjryRedBwuU. These codes are open source codes that do not require licencing. Model outputs are available upon request to Ji-Won Lee (leejiwon2040@knu.ac.kr).
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC1
RC1:
'Comment on gmd-2022-18', Anonymous Referee #1, 16 May 2022
Review of Manuscript # gmd-2022-18 in GMDD: “Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases” by Min et al.

General comments:

The authors examined the performance of two different radar data assimilation methods, LETKF and 3DVAR, in the two snowfall cases observed during ICE-POP 2018 field campaign. They found the two methods produced different analyses and forecasts in winds, water vapor, temperature, and snowfall. They concluded that assimilating water vapor is important in radar assimilation, which impacts precipitation forecasts significantly. Overall, the manuscript is well organized and easy to follow, and the logic is clear. However, there are several concerns that should be clarified before considering the manuscript for publication.

Specific comments:

The authors compared two very different radar DA methods. Therefore, there might be opposite results attained when tuning the parameters, e.g., number of ensemble members. It might be not suitable to tell which method is better. Especially, it seems that the authors used different radar reflectivity operators in the two methods. Please justify it. How about the results if the authors use the same observation operators for radar radial velocity and reflectivity in the two DA methods? How about the results if LETKF also assimilates water vapor indirectly not through covariance based on ensemble members?

If the reviewer understood correctly, only one forecast was produced in each snowfall case in each DA experiment. The reviewer would suggest that one forecast can be produced in each DA cycle, and then there are enough samples for the authors to conduct statistical evaluation (RMSE, FSS, etc.), which will make a solid study.

Sections 3.2, 3.3, and 3.5: Please conduct quantitative analysis and comparison, not just full of “large” “more” “similar” “underestimate” “increase” “relatively small/dry” “low” …

Lines 303-314: What spatial scale did the authors use to calculate FSS? Please examine the sensitivity of different spatial scales and precipitation thresholds.

Figures 12-15: How about the results of CTRL? Please include them for comparison.

Technical corrections:

Line 120: “component” -> “components”

Line 125: “BE” -> “background error”

Line 126: Please justify the observation errors used in this study.

Table 1: Please provide a brief description of prognostic variables.

Line 188: “compare” -> “examine”

Lines 194-195: Did the authors interpolate station data to the model grid?

Lines 195 and 201: “using” -> “by”

Line 196: “fractions skill score” -> “fractions skill score (FSS)”

Line 206: “at 3 km” means “at 3-km height” or “at the 3-km domain”

Lines 218-220: Please conduct a quantitative comparison.
Citation: https://doi.org/10.5194/gmd-2022-18-RC1
- AC2: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC2
- AC5: 'Reply on RC1', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC5
RC2:
'Comment on gmd-2022-18', Anonymous Referee #2, 21 May 2022

see attachment.

Citation: https://doi.org/10.5194/gmd-2022-18-RC2
- AC3: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC3
- AC6: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC6
- AC7: 'Reply on RC2', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC7
RC3:
'Comment on gmd-2022-18', Anonymous Referee #3, 31 May 2022
General comments:

The paper uses radar data to run two different data assimilation methods, LETKF and 3dVAR, during two snowfall events observed in ICE-POP 2018 fleld campaign. The authors compared the analysis and the forecasted data of different variables including wind, water vapor, temperature, and snowfall to show the importance of water vapor assimilation. The logic and structure of the paper are clear and easy to follow. However, several sentences need to be rewritten/reconsidered as well as some concerns which need to be considered before publication.

Specific comments:

It seems the authors run only one forecast cycle to compare two methods which cause few samples to do the verification methods. Please consider running more forecast cycles to have enough samples which make the verification as well as the results more reasonable.

The authors use two different data assimilation methods; however, in many sentences particularly in the results and summary sections, the "simulation" word was used to refer to the LETKF or 3DVAR methods. Please note that this is the wrong word referring to the assimilation method. The LETKF and 3DVAR do the "assimilation" not "simulation". Sentences like „The snowfall in GWD was less simulated in LETKF“ are logically wrong and need to be rewritten.

The word "underestimate" was used many times in sections 3 and 4 to compare two assimilation methods. Since non of these methods were considered as a reference experiment or reference data, using the words "underestimation“ or "overestimation“ for this comparison is meaning less. The words "underestimation" or "overestimation" could be used when the results are compared with reference data such as observation. Please consider rewriting these sentences.

In section 2.2.2 the radial wind and the reflectivity errors are assumed 3 ms-1 and 5 dbz respectively; however, the authors did not mention the source of these numbers.

In section 2.3 was mentioned that the precipitation up to 24.8 mm was recorded in the red box area from 00 to 12 UTC. It is an unclear sentence. The precipitation reported from an SYNOP station (which probably is the concern of the author in this sentence) would be for a specific point not for a whole specific area. The sentence could be rewritten by pointing to the minimum and maximum precipitation amount in this area as well as the location of the station which had a maximum report of precipitation. Please consider also the last paragraph in this section which has the same problem.

In section 3.1 the sentence „The snow mixing ratio is higher in LETKF“ is a general sentence that of course is not generally correct. Please consider mentioning clearly about the specific case (time/location) where the assimilated snow mixing ratio is higher than assimilated snow mixing ratio in 3DVAR.

In Fig. 8, there is no explanation for the dashed lines.

Fig. 14(d), there is a weird feature regarding the FSS score. The FSS scores for both LETKF and 3DVAR methods are very low at the first forecast hours and they increase after about 6 hours. This is not a common behavior in the FSS score of a forecast validation. It would be good if the authors recheck this case or explain a bit about such a weird behavior of the FSS score.

Technical correction:

Line 35: „cool ocean winds“ → „cold ocean winds“

Line 49: „has“ → „get“

Line 69: „only include information → „include only information“

Line88: „Further“ → „Furthermore“

Line 141: „improves“ → „calculates“. Please consider changing all other „improve “ in this paragraph and the next one to „calculate“ or „produce“

Line 206: „at 3 km“, 3km resolution? Or 3km height? Please specify it.

Line 208: The sentence „Increment in wind and hydrometeors show similar patterns, depending on the DA method“. Please consider rewriting this and the similar sentences in this section. The sentence is unclear and ambiguous.

Line 235: „,the LETKF underestimates the temperature,“ → „.there is an underestimation in the temperature derived from the LETKF method,“

Line 253: „Note the amount of change in the snow mixing ratio“, what is the point of this sentence?

Line 278: „The observed GRS radar CFAD“ → „The observed CFAD of the GRS radar“

Line 294: „In Case 1, The“ → „In case 1, the“

Line 305: „hour prediction“ → „forecast hours“ please also consider replacing the „prediction“ with „forecast“ in this and the next section.
Citation: https://doi.org/10.5194/gmd-2022-18-RC3
- AC4: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
  
  We thank the reviewer’s detailed suggestions and constructive remarks. The authors addressed all of the points made by the reviewer and included a point-by-point response to the reviewer’s comments as an attachment.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC4
- AC8: 'Reply on RC3', Ki-Hong Min, 20 Jun 2022
  
  As an appendix, we have additionally written about microphysical process budgets and the reflectivity operator.
  
  Citation: https://doi.org/10.5194/gmd-2022-18-AC8

Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee

Viewed

Total article views: 1,194 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
822	311	61	1,194	34	38

HTML: 822
PDF: 311
XML: 61
Total: 1,194
BibTeX: 34
EndNote: 38

Views and downloads (calculated since 06 Apr 2022)

Month	HTML	PDF	XML	Total
Apr 2022	144	20	4	168
May 2022	101	33	8	142
Jun 2022	114	35	14	163
Jul 2022	36	16	1	53
Aug 2022	25	15	1	41
Sep 2022	36	18	0	54
Oct 2022	14	2	1	17
Nov 2022	13	3	0	16
Dec 2022	7	4	0	11
Jan 2023	14	4	0	18
Feb 2023	19	14	0	33
Mar 2023	21	3	0	24
Apr 2023	18	7	1	26
May 2023	8	5	1	14
Jun 2023	6	2	1	9
Jul 2023	7	20	0	27
Aug 2023	8	5	0	13
Sep 2023	31	10	2	43
Oct 2023	18	11	0	29
Nov 2023	11	8	0	19
Dec 2023	16	12	3	31
Jan 2024	6	3	1	10
Feb 2024	16	14	3	33
Mar 2024	19	22	5	46
Apr 2024	29	5	4	38
May 2024	8	4	3	15
Jun 2024	7	2	1	10
Jul 2024	8	3	1	12
Aug 2024	16	2	2	20
Sep 2024	9	0	9
Oct 2024	8	2	2	12
Nov 2024	25	6	2	33
Dec 2024	4	1	0	5

Cumulative views and downloads (calculated since 06 Apr 2022)

Month	HTML	PDF	XML	Total
Apr 2022	144	20	4	168
May 2022	101	33	8	142
Jun 2022	114	35	14	163
Jul 2022	36	16	1	53
Aug 2022	25	15	1	41
Sep 2022	36	18	0	54
Oct 2022	14	2	1	17
Nov 2022	13	3	0	16
Dec 2022	7	4	0	11
Jan 2023	14	4	0	18
Feb 2023	19	14	0	33
Mar 2023	21	3	0	24
Apr 2023	18	7	1	26
May 2023	8	5	1	14
Jun 2023	6	2	1	9
Jul 2023	7	20	0	27
Aug 2023	8	5	0	13
Sep 2023	31	10	2	43
Oct 2023	18	11	0	29
Nov 2023	11	8	0	19
Dec 2023	16	12	3	31
Jan 2024	6	3	1	10
Feb 2024	16	14	3	33
Mar 2024	19	22	5	46
Apr 2024	29	5	4	38
May 2024	8	4	3	15
Jun 2024	7	2	1	10
Jul 2024	8	3	1	12
Aug 2024	16	2	2	20
Sep 2024	9	0	9
Oct 2024	8	2	2	12
Nov 2024	25	6	2	33
Dec 2024	4	1	0	5

Viewed (geographical distribution)

Total article views: 1,114 (including HTML, PDF, and XML) Thereof 1,114 with geography defined and 0 with unknown origin.

Country	#	Views	%

Cited

Latest update: 13 Dec 2024

Short summary

LETKF underestimated the water vapor mixing ratio and temperature compared to 3DVAR due to a lack of a water vapor mixing ratio and temperature observation operator. Snowfall in GWD was less simulated in LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor mixing ratio and temperature in radar DA.


Total:	0
HTML:	0
PDF:	0
XML:	0

Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases

Viewed

Viewed (geographical distribution)

Cited

1 citations as recorded by crossref.