update

auxdata/docs/userguide/content/ReleaseNotes.html

@@ -64,8 +64,7 @@
     working multi variables cross-section displays, we recommend using a color-filled contour display or
     a color-shaded display, and contour displays for the second or the third variables. Multi-variable cross-section
     displays offer several advantages in data visualization and analysis, such as enhanced data comparisons,
-    comprehensive analysis, and efficient use of screen space. Additionally, you can now switch the vertical
-    coordinate scale from meters to pressure in hPa, providing greater flexibility in interpreting the data.
+    comprehensive analysis, and efficient use of screen space.
   </p>
 </faqitem>
 
@@ -75,9 +74,7 @@
     the available data, with time as the independent coordinate (x-axis). You can choose between contour,
     color-filled, and color-shaded time-height displays. After creating the time-height display for the
     first variable, you can add a contour time-height display for a second variable. This setup allows
-    for a more detailed and layered analysis of vertical atmospheric data over time. Additionally, you can
-    now switch the vertical coordinate scale from meters to pressure in hPa, providing greater flexibility
-    in interpreting the data.
+    for a more detailed and layered analysis of vertical atmospheric data over time.
   </p>
 </faqitem>
 
@@ -96,32 +93,24 @@
 <faqitem cat="Jython Changes"></faqitem>
 
 
-<faqitem id="classifierformulas" q="Classifier">
+<faqitem id="gdiformulas" q="Galvez-Davison Index">
   <p>
-    Data classification helps in organizing data into categories based on sensitivity, importance, or other
-    criteria. In machine learning, classification involves assigning a class label to a given input data example.
-    The Jython Classifier formula provides a simple method to label 2D gridded data values with numbers.
-    This basic classification algorithm is used to sort data points into different classes, allowing machine
-    learning applications to train on existing data and predict the classification of new data points.
+    The Gálvez-Davison Index (GDI) is a stability index developed to enhance the accuracy of thunderstorm predictions
+    and predict shallower forms of moist convection in tropical regions. The newly introduced formula calculates GDI
+    using temperature and relative humidity 3D grid fields.
   </p>
 </faqitem>
 
-<faqitem id="medianfilterformulas" q="Median Filter">
+<faqitem id="dryformulas" q="Dry Static Energy">
   <p>
-    A median filter is a non-linear digital filtering technique widely used in image processing to reduce noise.
-    It works by replacing the center pixel in a neighborhood with the median value of that surrounding window.
-    This technique is particularly effective at removing Gaussian, random, and salt-and-pepper noise from images.
-    Due to its effectiveness, the median filter is commonly used in the data preprocessing stage of machine
-    learning applications.
+    The new grid diagnostics formula calculates Dry Static Energy with Temperature and Geopotential
+    Height 3D grid fields.
   </p>
 </faqitem>
 
-<faqitem id="2dfunctiongrid" q="2D function over grid">
+<faqitem id="2d3dgrid" q="3D and 2D grid subtract and multiply">
   <p>
-    Applying spatial functions like Max, Min, Average, and Percentile over a 2D grid allows you to generate
-    a new time series point field. By applying these functions to the 2D grid, you transform spatial data into
-    a time series that reflects specific statistical properties, offering new insights into the data’s temporal
-    dynamics and preprocessing the data of machine learning applications.
+    The new grid diagnostics formula performs a vertical subtraction/multiplication of a 2D grid from a 3D grid.
   </p>
 </faqitem>