Forums

Back

Probabilistic Forecast of Thunderstorms Using 2-D Convolutional Neural Network (CNN) - June VLab Forum

JS

VLab Forum Members,

The June 2023 VLab Forum will occur on Thursday, the 22nd, at 3:35 PM – 4:30 PM (Eastern Time). The talk features a presentation titled "Probabilistic Forecast of Thunderstorms Using 2-D Convolutional Neural Network (CNN)" which is being presented by Mamoudou Ba. We hope you can attend.

To participate in the forum, please register for the webinar.

Abstract:

A supervised 2-Dimensional (2-D) Convolutional Neural Network (CNN) model was developed by training the model with 5 years (28 September – 31 October 2016, and March 1 – 31 October 2017 - 2020) of predictors derived from the High-Resolution Rapid Refresh (HRRR) model to produce short range (1 - 8h) CNN Probability of Thunderstorm Forecasts (CNNPTFs) over the conterminous United States (CONUS). An architecture of three hidden layers of 80 features detection maps each is used. Thirteen HRRR variables that provided the most accurate predictions among 22 predictors experimented with during the development of the algorithm were selected. Multi-Radar/Multi-Sensor System (MRMS) Composite Reflectivity (MRMSCRs) values were used as truth data to train the model. CNNPTFs were produced over our validation period (1 March - 31 October 2021 and 2022) at each HRRR model’s initiation time. Qualitative validation was performed by comparing CNNPTFs to MRMSCRs for several case studies during the validation period. Finally, traditional statistical metrics are computed to objectively verify the skill of CNNPTFs and compare it performance to that of the HRRR Simulated Composite Reflectivity Forecasts (HRRRSCRFs). Compared to HRRRSCRFs over our validation period the performance of CNNPTFs generally exceeds that of HRRRSCRFs.

In this presentation, the CNN technique will be discussed, the methodology will be presented, and results showing the skill of the CNN will be shown. While this research is being undertaken with the goal of improving MDL’s thunderstorm forecast guidance, the AI CNN technique has potential application to other post-processing work being done in MDL.

Agenda:

You can find the agenda for the Forum at the following link:

Add to Your Calendar:

To add this VLab Forum meeting to your calendar, please click on the following button.

Unsubscribe/subscribe to VLab Forum announcements (You must be logged into the VLab)

VLab Forum Members,

Just a quick reminder that the June 2023 VLab Forum will occur this Thursday, the 22nd, at 3:35 PM – 4:30 PM (Eastern Time). The talk features a presentation titled "Probabilistic Forecast of Thunderstorms Using 2-D Convolutional Neural Network (CNN)" which is being presented by Mamoudou Ba. We hope you can attend.

To participate in the forum, please register for the webinar.

Abstract:

A supervised 2-Dimensional (2-D) Convolutional Neural Network (CNN) model was developed by training the model with 5 years (28 September – 31 October 2016, and March 1 – 31 October 2017 - 2020) of predictors derived from the High-Resolution Rapid Refresh (HRRR) model to produce short range (1 - 8h) CNN Probability of Thunderstorm Forecasts (CNNPTFs) over the conterminous United States (CONUS). An architecture of three hidden layers of 80 features detection maps each is used. Thirteen HRRR variables that provided the most accurate predictions among 22 predictors experimented with during the development of the algorithm were selected. Multi-Radar/Multi-Sensor System (MRMS) Composite Reflectivity (MRMSCRs) values were used as truth data to train the model. CNNPTFs were produced over our validation period (1 March - 31 October 2021 and 2022) at each HRRR model’s initiation time. Qualitative validation was performed by comparing CNNPTFs to MRMSCRs for several case studies during the validation period. Finally, traditional statistical metrics are computed to objectively verify the skill of CNNPTFs and compare it performance to that of the HRRR Simulated Composite Reflectivity Forecasts (HRRRSCRFs). Compared to HRRRSCRFs over our validation period the performance of CNNPTFs generally exceeds that of HRRRSCRFs.

In this presentation, the CNN technique will be discussed, the methodology will be presented, and results showing the skill of the CNN will be shown. While this research is being undertaken with the goal of improving MDL’s thunderstorm forecast guidance, the AI CNN technique has potential application to other post-processing work being done in MDL.

Agenda:

You can find the agenda for the Forum at the following link:

Add to Your Calendar:

To add this VLab Forum meeting to your calendar, please click on the following button.

Unsubscribe/subscribe to VLab Forum announcements (You must be logged into the VLab)

VLab Forum Members,

The slides for the today's VLab Forum are now available for your review. The talk will take place at 3:35 PM – 4:30 PM (Eastern Time) and features a presentation titled "Probabilistic Forecast of Thunderstorms Using 2-D Convolutional Neural Network (CNN)" which is being presented by Mamoudou Ba. We hope you can attend.

To participate in the forum, please register for the webinar.

Abstract:

A supervised 2-Dimensional (2-D) Convolutional Neural Network (CNN) model was developed by training the model with 5 years (28 September – 31 October 2016, and March 1 – 31 October 2017 - 2020) of predictors derived from the High-Resolution Rapid Refresh (HRRR) model to produce short range (1 - 8h) CNN Probability of Thunderstorm Forecasts (CNNPTFs) over the conterminous United States (CONUS). An architecture of three hidden layers of 80 features detection maps each is used. Thirteen HRRR variables that provided the most accurate predictions among 22 predictors experimented with during the development of the algorithm were selected. Multi-Radar/Multi-Sensor System (MRMS) Composite Reflectivity (MRMSCRs) values were used as truth data to train the model. CNNPTFs were produced over our validation period (1 March - 31 October 2021 and 2022) at each HRRR model’s initiation time. Qualitative validation was performed by comparing CNNPTFs to MRMSCRs for several case studies during the validation period. Finally, traditional statistical metrics are computed to objectively verify the skill of CNNPTFs and compare it performance to that of the HRRR Simulated Composite Reflectivity Forecasts (HRRRSCRFs). Compared to HRRRSCRFs over our validation period the performance of CNNPTFs generally exceeds that of HRRRSCRFs.

In this presentation, the CNN technique will be discussed, the methodology will be presented, and results showing the skill of the CNN will be shown. While this research is being undertaken with the goal of improving MDL’s thunderstorm forecast guidance, the AI CNN technique has potential application to other post-processing work being done in MDL.

Agenda:

You can find the agenda for the Forum at the following link:

Slides:

You can find the slides for the Forum at the following link:

Add to Your Calendar:

To add this VLab Forum meeting to your calendar, please click on the following button.

Unsubscribe/subscribe to VLab Forum announcements (You must be logged into the VLab)

VLab Forum Members,

For those of you who were unable to attend yesterday's VLab Forum, I have posted a recording of the talk titled "Probabilistic Forecast of Thunderstorms Using 2-D Convolutional Neural Network (CNN)" to the following VLab page.

VLab Forum talks and their recordings