CRTF Telecon - 11-26-2013

Insert slide #, and mention it during your presentation.
OLR-based method overestimated precipiation, particularly over the anvil region of deep convective clouds based on our recent study (Stenz, Dong, Xi, 2013, Assessment of SCaMPR and NEXRAD Q2 precipitation estimates using Oklahoma MESONET observations. Submitted to J. Hydrometeorology.
ABSTRACT: As Deep Convective Systems (DCS’s) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This paper assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. Q2 pixel-level estimates were directly compared to the collocated OK MESONET observations from 2010-2012. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations (~690 mm), there were very strong correlations between these two data sets for multiple temporal and spatial scales. SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations to OK MESONET observations during 2012. The severe overestimations from SCaMPR retrievals were primarily attributed to false alarm retrieval of heavy precipitation in anvil regions during DCS events. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS.

My group can provide NEXRAD Q2 precipitation over contiental USA from 2010-2012 and OK MESONET precipitation for last three decades.

My group can also provide global SW and LW fluxes (1x1 degrees) derived from NASA CERES Science Team, validated by surface observations.

Thanks Lisan, A very good analysis.

I know I've seen it for a long time, but MERRA's biases at the warm currents in E are quite obvious. If I can find that published somewhere, especially with some analysis why, I'll post here.

Slide 12 with the diagram that looks like a Taylor diagram, I think the radial axis should be something like a normalized standard deviation. RMS has influence from the bias, which affects the geometry that goes into the formulation of the diagram. I'm not sure that it will change the results, but it may be worth checking Taylor's paper (I don't presently have it in front of me).

A particular windmill I've been tilting at is the closing of budgets in reanalysis. Especially representing the analysis increment in reanalysis budgets. While MERRA explicitly includes the analysis increment, it can be estimated as a residual from the other reanalyses. It is a significant term over the ocean, and affects the representation of E-P. I mention it, because I feel that studies going into the future need to consider this more carefully, and also, reanalyses developers should provide this term of the budget.