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--- atmos:citation:research:research:iccp-cloudprobe2024_instructions [2024/06/28 18:40] – delene
+++ atmos:citation:research:research:iccp-cloudprobe2024_instructions [2024/06/28 20:22] (current) – delene
@@ Line 20: / Line 20: @@
   - Extract information from the header. Generate a PbP file that contains the image time from  header for each particle, count in buffer, W, L, A, D, P, end diode shadow flag.
   - Determine counts recorded by probe as function of time before any corrections for particle size are made or before the sample volume is computed.
-    - Complete images only
+    - Complete images only ''(Assume All-in Particles''
-    - Partial images only
+    - Partial images only ''(Assume Particles not All-in''
-    - Complete and partial images
+    - Complete and partial images ''(Assume sum of the above two''
-UPDATE: We are looking for total counts per second here and not a count size distribution, since the size distribution is generated in step 4.
+      * UPDATE: We are looking for total counts per second here and not a count size distribution, since the size distribution is generated in step 4.
-  - Determine counts recorded by probe as a function of time after corrections for particle size based on out of focus particles are made
+  - Determine counts recorded by probe as a function of time after corrections for particle size based on out of focus particles are made ''(Assume we need to use "water-processing flag in SODA)''
-    - Complete images only
+    - Complete images only ''(Assume All-in Particles''
-    - Partial images only
+    - Partial images only ''(Assume Particles not All-in''
-    - Complete and partial images
+    - Complete and partial images ''(Assume sum of the above two''
   - Number distribution functions before corrections for particle size (we will use counts per bin per second so that assumptions about the sample volume dependence on maximum dimension don't affect the results).
-    - Use 1-second resolution for generating counts;
+    * Use 1-second resolution for generating counts;
-    - Bins should have widths corresponding to 1 pixel
+    * Bins should have widths corresponding to 1 pixel
-    - Entries should correspond to number of counts in that bin for given second (should be integer)
+    * Entries should correspond to number of counts in that bin for given second (should be integer)
-    - Files should cover entire length of time in synthetic data files
+    * Files should cover entire length of time in synthetic data files
-    - Should include complete and partial images
+    * Should include complete and partial images
-UPDATE: In order for these size distributions to be comparable, we need to use the same definition of dimension. I suggest that we use maximum particle dimension so that we are all using the same definition (Note that there still may be some differences because there are different algorithms for defining maximum dimension).  Note also that the simulations have a constant PSD over the full minute, so it is probably easier to aggregate everything into a single distribution rather than breaking down by the second.  However, we can handle data submitted either way.
+      * UPDATE: In order for these size distributions to be comparable, we need to use the same definition of dimension. I suggest that we use maximum particle dimension so that we are all using the same definition (Note that there still may be some differences because there are different algorithms for defining maximum dimension).  Note also that the simulations have a constant PSD over the full minute, so it is probably easier to aggregate everything into a single distribution rather than breaking down by the second.  However, we can handle data submitted either way.
   - Number distribution function after corrections for particle sizes (we will specify a priori the sample volume)
     - Use same instructions as in step 4 and include complete and partial images
-    - Number distribution function after removal of artifacts (based on particle morphology)
+  - Number distribution function after removal of artifacts (based on particle morphology)
-    - Number distribution function after removal of artifacts (based on particle morphology) and shattered particles (based on interarrival time)
+  - Number distribution function after removal of artifacts (based on particle morphology) and shattered particles (based on interarrival time)
   - Number distribution function after removal of artifacts (based on particle morphology) and shattered particles (based on interarrival time) and reacceptance of spuriously removed particles.
-UPDATE: Aaron reminded me that there is no shattering in the simulated datasets.  Thus, steps 7/8 will probably not yield much in results (though if you have time to complete these steps, you will see how many non-artifact particles get removed!)
+    *UPDATE: Aaron reminded me that there is no shattering in the simulated datasets.  Thus, steps 7/8 will probably not yield much in results (though if you have time to complete these steps, you will see how many non-artifact particles get removed!)
 It is possible we might not have time to complete all steps before the workshop. Please send your files after we complete each step so that we can intercompare the distributions on a step-by-step basis. We anticipate that these comparisons will be taking place continuously before we meet in Jeju to discuss the final comparisons. It will be very helpful to start receiving files soon.