MPAS Model Cycling Issue: Investigation And Expert Input
Hey guys,
We've recently stumbled upon a potential model cycling issue that seems to be making a comeback, and we wanted to share our findings and get some expert input. This involves the UFS-Community/MPAS-Model, and we're diving deep into it.
The Mystery: Cycling Forecasts vs. Cold Start Forecasts
Our main concern revolves around the difference between hourly "mpasout" cycling forecasts (without Data Assimilation - NoDA) and cold start forecasts. Ideally, these two should be pretty darn close, especially when looking at the same validation time. However, Haidao, Chunhua, Ruifang, and I have all noticed a significant discrepancy. This suggests that the model cycling issue might be resurfacing, which is something we definitely need to address. This means model cycling must be accurate for reliable predictions.
Initial Findings and Testing
To get a clearer picture, I ran tests using the latest ufs-community/MPAS-Model (version v8.3.0-1.13). I made a tweak by removing the packages attributes for relevant variables in the da_state stream. This was done to eliminate those pesky warning messages we saw earlier in #150. The goal here was to ensure that the mutable/immutable choice of da_state wouldn't throw off our cycling test results. We want to isolate the real issue, right? The stability of the UFS-Community/MPAS-Model is crucial for accurate weather forecasting. Therefore, understanding potential issues like this is paramount for improving model performance and reliability.
Diving Deeper: MATS Verification and Sounding Data
To visually demonstrate the issue, I've put together some slides from the MATS verification. These slides compare the cycling (NoDA) 1-hour forecasts from 11/23z with the cold start 12-hour forecasts from 00/12z. Take a look at the presentation here:
For a quick visual, here’s the verification plot against the sounding data:
As you can see, there are noticeable differences that raise concerns. Ensuring the accuracy of forecasts is vital for various applications, ranging from daily weather updates to long-term climate predictions. These discrepancies can impact the reliability of weather advisories and planning, underscoring the importance of addressing model cycling issues.
Next Steps and Call for Expert Input
We're not stopping here. We're planning to conduct more tests to really dig into the root cause of this issue. But, we believe that getting insights from you, the model experts, would be incredibly valuable. Your experience and perspectives could help us identify potential causes and solutions more efficiently. So, we’re reaching out to get your thoughts and any suggestions you might have.
We're especially keen on hearing from @clark-evans @barlage @AndersJensen-NOAA @joeolson42 @hu5970. Your expertise is highly appreciated!
Understanding Model Cycling and Cold Start Forecasts
Before diving deeper into our investigation, let's clarify the difference between model cycling and cold start forecasts. This distinction is crucial for understanding the potential implications of the discrepancies we've observed. Model cycling and cold start forecasts are two distinct approaches to weather prediction, each with its own strengths and weaknesses. The differences in their setup and execution can lead to variations in forecast accuracy, particularly in the short term.
What is Model Cycling?
In model cycling, the forecast from one model run serves as the initial condition for the next run. Imagine it like a relay race where the baton (the forecast) is passed from one runner (model run) to the next. This process is continuous, with each new forecast building upon the previous one. The advantage of model cycling is that it can capture evolving atmospheric conditions more smoothly. By continuously updating the initial state, the model can potentially track fast-changing weather phenomena more accurately. Continuous updates are the backbone of the model cycling approach.
What are Cold Start Forecasts?
On the other hand, cold start forecasts begin from scratch using observational data as the initial condition. Think of it as starting a new race every time, without any prior momentum. These forecasts typically rely on data assimilation techniques to ingest observations and create the best possible initial state. While cold starts can benefit from the most up-to-date observational data, they might miss some of the continuity captured by cycling. Initial conditions are the key to the cold start forecasts methodology.
Why Should They Be Close?
Ideally, cycling forecasts and cold start forecasts valid at the same time should be quite similar, especially in the short term. This is because both are trying to predict the same atmospheric state, albeit from slightly different starting points and methods. Significant differences between these forecasts can indicate an issue within the model cycling process, such as the accumulation of errors or inconsistencies in how the model handles continuous updates. The consistency of forecasts validates model reliability.
The Implications of Discrepancies
When cycling and cold start forecasts diverge significantly, it raises a red flag about the stability and reliability of the model. It suggests that errors might be propagating and amplifying through the cycling process, leading to inaccurate predictions. This is particularly concerning for short-range forecasting, where timely and precise information is crucial for decision-making in various sectors, including aviation, agriculture, and emergency management. Accuracy impacts real-world decisions significantly.
Diving Deeper: The Technical Details and Our Testing Approach
Now, let’s delve into the more technical aspects of our investigation and the specific steps we've taken to identify this potential issue. This involves understanding the model configuration, the changes we made, and the rationale behind our testing methodology. A thorough understanding of the model and its components is crucial for effective troubleshooting. Detailed analysis ensures accuracy and reliability.
The UFS-Community/MPAS-Model and Version v8.3.0-1.13
We're focusing on the ufs-community/MPAS-Model, which is a cutting-edge atmospheric model used for a wide range of weather forecasting applications. The specific version we're working with is v8.3.0-1.13. This version incorporates several updates and improvements, making it a robust platform for our investigation. Staying updated with the latest model version is essential for leveraging the newest advancements.
The da_state Stream and Mutable/Immutable Choices
One key area of interest is the da_state stream within the model. This stream is responsible for handling data assimilation states, which are crucial for cycling forecasts. In previous discussions, we encountered warning messages related to mutable/immutable choices in the da_state. To ensure these warnings weren't influencing our results, I took a specific step: I removed the packages attributes for relevant variables in the da_state stream. This was a deliberate attempt to isolate the cycling issue from any potential side effects caused by these mutable/immutable settings. Isolating variables ensures accurate test results.
Why Remove the packages Attributes?
By removing the packages attributes, we aimed to eliminate any ambiguity or potential conflicts arising from how data was being handled within the da_state stream. This allowed us to focus squarely on the core cycling mechanism and whether it was functioning as expected. The primary goal was to isolate and eliminate potential error sources. A clean environment enhances model accuracy.
The MATS Verification and Sounding Data: A Visual Comparison
To visualize the differences between cycling and cold start forecasts, we turned to the MATS (Model Analysis Tool Suite) verification. MATS provides a comprehensive set of tools for evaluating model performance against observational data. We compared 1-hour cycling (NoDA) forecasts from the 11/23z cycle with 12-hour cold start forecasts from 00/12z. This comparison is particularly informative because it highlights discrepancies over a relatively short forecast horizon, where the two forecast types should ideally align closely. Short-term disparities indicate core model issues.
Verification Plots Against Sounding Data
The verification plot against sounding data provides a direct visual representation of how well the model forecasts match actual atmospheric conditions. Sounding data, obtained from weather balloons, offers a detailed vertical profile of temperature, humidity, and wind. By comparing model forecasts to these profiles, we can identify specific areas where the model is underperforming. Observational data grounds model performance analysis.
What the Verification Plot Shows
The verification plot clearly shows differences between the cycling and cold start forecasts. These differences are not subtle; they are significant enough to warrant further investigation. The plot serves as a compelling piece of evidence that there is indeed a potential cycling issue within the model. Visual data underscores the issue's significance.
The Road Ahead: Further Testing and Expert Insights
Our investigation is far from over. We’re committed to thoroughly understanding and resolving this potential model cycling issue. This requires a multifaceted approach that includes additional testing, deeper analysis, and, crucially, input from the expert community. Expert insight guides future actions.
More Tests on the Horizon
We have several tests planned to further pinpoint the root cause of the discrepancies we've observed. These tests will likely involve varying model configurations, examining different variables, and extending the forecast horizon. The goal is to systematically isolate the factors contributing to the cycling issue. Systematic testing identifies root causes effectively.
Digging Deeper into the Code
In addition to running more tests, we'll also be diving deeper into the model code. This involves examining the specific routines and algorithms that handle cycling and data assimilation. We’ll be looking for any potential bugs, inconsistencies, or areas where the code might be optimized. Code review ensures model integrity.
The Value of Expert Input
As we move forward, the input from model experts like yourselves is invaluable. Your collective experience and knowledge can help us identify potential areas of concern that we might otherwise overlook. We believe that a collaborative approach is the most effective way to tackle complex issues like this. Collaboration maximizes problem-solving potential.
Specific Questions and Areas of Focus
We have a few specific questions and areas of focus where we’d particularly appreciate your insights:
- Have you encountered similar cycling issues in previous versions of the model?
- Are there specific model configurations or settings that might be contributing to this issue?
- Do you have any suggestions for tests or analyses that we should conduct?
Your answers to these questions, as well as any other insights you can offer, will be immensely helpful in guiding our investigation. Expert advice accelerates issue resolution.
We truly appreciate your time and expertise. Let's work together to get to the bottom of this! Collaboration ensures accurate model performance.