# Presentation: 515318

## TRMM Precipitation Radar Comparisons with WSR-88D Radar at Melbourne, FlR. Meneghini & L. Liao - Hydrospheric & Biospheric Sciences Laboratory, Instrumentation Sciences Branch (robert.meneghini-1@nasa.gov)

**Study:**
- 	Rain Rates obtained from 174 TRMM Radar overpasses of the Melbourne, Florida WSR-88D site were compared with estimates from ground-based radar data
**Findings:**
- Excellent agreement in means between TRMM & ground radars
- RMS errors on the order of  25-30% of the mean in both cases
- Despite mismatches of data sets in space & time, results suggest that spaceborne estimates of rain rate at this site are comparable in accuracy to a well-calibrated ground-based radar

## TRMM Precipitation Radar Comparisons with WSR-88D Radar at Melbourne, Fl

- TRMM Precipitation Radar Comparisons with WSR-88D Radar at Melbourne, Fl

**Comparison of area-average rain rates from space and ground radar over the area of intersection**

**Comparison of the means over all 4 km x 4 km elements for **
**which both radars detected rain (right-hand plot).  Size of **
**data point represents the number of raining elements**

**Means of the data are represented by the data point in yellow**

##   NASIRF: The NASA Air-Sea Interaction Research Facility

**  *****NASIRF: *****The NASA Air-Sea Interaction Research Facility**

**Steven R. Long**
**NASA GSFC / WFF**
**Code 614.2**
**Wallops Island, VA 23337**

**The Goal: **
***Accurate Measurements of:***
***     ******--net surface heat flux***
***     ******--net solar irradiance***
***     ******--near-surface temperature***
***     ******--sea state parameters***

***Joint Heat Flux Experiments with Western Connecticut State University: Jan.2006***
***                                                    ******Prof. James Boyle***

**Inside NASA Wind Wave Tank**
**WCSU Multi-Sensor Float Under Development (~ 20 cm diameter)**

**IR Image Looking Down Into Wave Tank at the New Multi-Sensor Float (MSF)**

**Flux Measurements During IR Image**

- Visit the Facility at http://nasirf.wff.nasa.gov

**Notes:**
A Multi-Sensor Float (MSF) for measuring ocean surface parameters and processes (“skin” temperature, heat flux, etc.). 
The experimental conditions used were cooled air (temperature and humidity controlled) flowing over heated water within the wind-wave tank. 
Top Left Image:  View of WCSU multi-sensor floating inside the NASA Air-Sea Interaction Research Facility wave tank. A light wind is blowing right to left. A Pitot rake is used to measure wind speed at 3 levels, seen at left of image, giving u*, the friction velocity. The multi-sensor float is a foam ring of about 20 cm diameter with a porous fiberglass fabric stretched across the ring opening, providing a porous fabric at the air-water interface. On this porous fabric surface, sensors are mounted that measure skin temperature gradients at the water surface, which ultimately determines net surface heat flux, net solar irradiance, and near surface temperatures. The float can sense inversion (flipping) by wave conditions, and changes the sign of data appropriately. A small instrument module can be seen on the ring perimeter.
Bottom Left Image: Looking down onto the water surface using an IR camera. This shows the temperature variation across the wave surface as the multi-sensor float moves by in the light wind wave field. Additionally, it gives another measure of the temperatures present within the ring at the air-water interface at a particular time during the temperature data measurement, as an additional verification of data quality.
Right-Side Double Chart: Upper plot shows time variation of heat flux in units of 100*watts/m^2. Black line is from bulk temperature measurements. Green line is from water surface temperature difference measurements. Notice it is more responsive to turbulent variations. In the lower plot of actual temperatures, the upper line of the three lines shows the bulk water temperature along with two separate measures of water surface temperature. The middle line gives the air temperature at 2 cm above the surface, and the bottom line gives the air temperature at 8 and 23 cm above the water surface. The air was cooled and flowed over a warmed water surface.
The scientific result is an accurate measurement of water surface skin temperature gradients, demonstrating an effective and inexpensive means of providing net surface heat flux, net solar irradiance, and near surface temperatures, along with sea state parameters. Many numerical models need to specify surface fluxes and temperatures as boundary conditions. The device could also provide local measurements to validate atmospheric corrections of satellite radiometric surface temperature data.

## Research and Development:

**A Surface Contact, Lightweight, Wave-Following,**
**Multi-Sensor Float**

**What is NASIRF?:****   **
**NASIRF, the NASA Air-Sea Interaction Research Facility at Wallops Flight Facility has hosted another joint experiment with the Western Connecticut State University and Prof. James Boyle. The instrument uses a porous fiberglass fabric stretched inside a foam ring to support the sensors at the air-water interface.**
**NASIRF Goal:**
**The Goal is to measure ocean surface skin temperature gradients, which are ultimately used to determine net surface heat flux, net solar irradiance, and near-surface temperature. The device is wave-following, allowing measurement of other sea state parameters. Accurate *****in situ *****measurements of local skin temperatures would support verification of the accuracy of atmospheric corrections for satellite radiometric measurements. Many numerical models need to specify surface fluxes and temperatures as boundary conditions. This new sensor concept is proving capable for this task.**
**        **
**Future Scalability:**
**Hundreds of these multi-sensor floats could be deployed from ships or aircraft to provide ground truth for satellite-based measurements and support for air-sea flux field experiments. Among the advantages of this instrument is its extreme low cost and simple operation****.**

**Steven R. Long**
**NASA GSFC / WFF**
**Code 614.2**
**Wallops Island, VA 23337**
**Steven.R.Long@nasa.gov**

**Prof. James P. Boyle**
**Assoc. Chair**
**Dept. of Physics & Meteorology**
**Western Connecticut State University**
**BoyleJ@wcsu.edu **

## Subglacial Lakes from ICESat and MOA, on the flanks of the  Gamburtsev Subglacial Mountains, East AntarcticaChristopher A. Shuman - Cryospheric Sciences Branch

**ICESat DEM and MOA Composite Image**

**Three-dimensional illustration of two recently defined (GRL 2005) subglacial lakes (90°E and Sovetskaya) on the flanks of the Gamburtsev Subglacial Mountains, East Antarctica from the MODIS Mosaic of Antarctica (MOA) image overlaid on the new ICESat 500m DEM. Subglacial Lake Vostok (at left) is larger and has previously been studied. **
***For more on these subglacial lakes: http://www.earth.columbia.edu/news/2006/story01-26-06.html***

**Gamburtsev**
**Subglacial**
**Mountains**

## Subglacial Lakes from ICESat and MOA, QueenMaud Land, East Antarctica

**View Angle in Fig. 1**

**ICESat DEM and MOA Composite Image**

**Fig. 1**

**Fig. 2**

**Three-dimensional illustration of a chain (A-D) of subglacial lakes in Queen Maud Land, East Antarctica from MOA imagery overlaid on the ICESat 500m DEM (Fig. 1).  The viewing angle (black arrow) and the onset of rapid ice flow (blue arrows) from these lakes are shown in the MOA image (Fig. 2). A distinct subglacial bedrock feature associated with the lakes is labeled ‘E’.**
***For more on MOA and the ICESat DEM: http://www.nasa.gov/centers/goddard/news/topstory/2005/antarctic_maps.html***

## Predicting lidar measured forest vertical structure from multi-angle spectral data

**What is Being Measured?:**
**A capability to remotely measure the vertical and spatial distribution of forest structure is required for more accurate modeling of energy, carbon, water, and climate over regional, continental, and global scales. **
**The potential of using a multi-angle spectral sensor JPL’s AirMISR (Airborne Multi-Angle Imaging Spectrometer) to predict forest vertical structure as measured by an airborne lidar system GSFC’s LVIS (Laser Vegetation Imaging Sensor).**
**Data and Measurements:**
**Data were acquired in 2003 for a 7000 ha forested study site near Howland Maine, **
** ****The LVIS data set provided a relatively direct measure of forest vertical structure at a fine scale (20 m diameter footprints). **
**Multivariate linear regression and neural network models were developed to predict the LVIS forest energy height measures from 28 AirMISR multi-angle spectral radiance values. **
**The best model accurately predicted the maximum canopy height (as measured from LVIS) using AirMISR data (rmse =0.92 m, R****2 ****=0.89). **
**The models developed in this study achieved high accuracies over a study site with an elaborate patchwork of forest communities with exceptional diversity in forest structure.**
**Conclusion**
** ****We conclude that models using MISR-like data are capable of accurately predicting the vertical structure of forest canopies when detailed ancillary information is available.**

- Kimes, Ranson/614.4Sun/UMCP, Blair/694

## Predicting lidar measured forest vertical structure from multi-angle spectral data

- LVIS and AirMISR Data

- AirMISR Image. Near- IR, red, and green channels from the 45.6o forward looking camera displayed as R,G,B.
- August 2003
- LVISImage LVIS heights at 100%, 50% and 25% return energy displayed as R,G,B.

- Images above are from Howland, Maine area.  Dark green areas on the AirMISR image are tall conifer forest, red areas are deciduous forest or grassy areas, light green or blue areas are non vegetated areas. The LVIS image shows no or short vegetation as black, taller mature forest stands as white and intermediate tones indicate structure differences.

- LVIS return waveform showing ground, maximum canopy height, H100 and  quartile heights H75, H50, and H25.

**Predicting lidar measured forest vertical structure**** from multi-angle spectral data**

- Multiangle AirMISR near-IR radiances for forest cover types. Camera angles: CA=60o aft, BA=45.6o aft,  AA=26.1o        AN=nadir,AF=26.1o  forward, BF=45.6o  forward, CF=60o forward.

- Kimes, Ranson/614.4Sun/UMCP, Blair/694

- Kimes et al., *Remote Sensing of Environment*, in press

- AirMISR predicted versus true LVIS H100 heights for a Neural network developed model. The model was applied to 1000 random test points. RMSE = 0.9m, R2= 0.89