# Presentation: 773306

## Hurricane Ivan:  15 Sept 04 approaching the Gulf Coast

- SeaWiFS view of Hurricane Ivan was collected at 1:50 PM Central Daylight Time as it approached the U.S. Gulf Coast.  The forth tropical storm to hit Florida in only 6 weeks.
- Credit line for all images:  Provided by the SeaWiFS Project, NASA Goddard Space Flight Center, and ORBIMAGE

- Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, SeaWiFS Project Office (gene.c.feldman@nasa.gov)

## Measuring Mount Moulton - West Antarctica

- As illustrated in the following slides, ICESat can measure specific ice sheet features with exceptional resolution and precision. ICESat's Track 1322 crosses the ocean surrounding Antarctica, to the Getz Ice Shelf, up the steep slopes of the Flood Range to the ice-covered summit of volcanic Mount Moulton, then across the West Antarctic ice sheet divide, and into the Ross Ice Shelf drainage basin. Despite variable atmospheric conditions during the three repeat-track acquisition times (note color breaks in the topographic profile due to thick clouds), the ability of ICESat to measure elevations across an ice sheet is clear. The second slide illustrates the ICESat data over just the summit portion of Mount Moulton as well as a proxy for the cloud cover at the time of acquisition (gain number). The data from the Laser 2a period was acquired through relatively clear skies as indicated by the lowest gain numbers. The data from the Laser 2c period was acquired when significant clouds wreathed the summit, especially on the north (left) side. The Laser 2b acquisition appears to have been through some clouds across the summit that increased to the south (right) side of the summit.

- The surveyed elevation of the mountain and the repeat track elevation data show general similarity but also some differences too. Cloud condition variations probably account for most of the observed elevation differences as clouds cause a slower return time for the light pulses relative to a clear sky. This 'scattering delay' causes apparent elevations to be below their true elevations. Work continues to identify and compensate for the impact of clouds on ICESat’s precise altimetry data. Other factors impacting ICESat’s ability to measure ice sheet features and change are also ongoing.

**Measuring Mount Moulton - West Antarctica**

- Christopher Shuman/NASA GSFC, Laboratory for Hydrospheric Processes, Oceans and Ice Branch
- (christopher.a.shuman@nasa.gov)

## Measuring Mount Moulton - West Antarctica

**Feature Name: Mount Moulton Lat: 76°03’S - Long: 135°08’W**
**Broad, ice-covered mountain 3,070 m high, standing 10 mi E of Mount Berlin, Flood Range, Marie Byrd Land, West Antarctica**

**ICESat Track 1322 has been acquired during each GLAS Laser 2 ops period although cloud conditions have impacted data quality/continuity**

## Measuring Mount Moulton - West Antarctica

**The Track 1322 geoid-corrected data are  ~5 m off of the best summit elevation known and may be offset laterally from the true summit.  Gain #s suggest 2b and 2c data was acquired through some cloud cover around the summit, note vertical exaggeration in plot above).  **

**Feature Name: **
**Mount Moulton **
**Lat: 76°03’S **
**Long: 135°08’W**
**Broad, ice-covered mountain 3,070 m high, standing 10 mi E of Mount Berlin, Flood Range, Marie Byrd Land, West Antarctica**

**Mount Moulton Summit - courtesy of Nelia Dunbar, New Mexico Tech, taken during recent NSF field work**

## Continuing Rapid Decline of the Arctic Perennial Ice Cover

- Joey Comiso/NASA GSFC, Laboratory for Hydrospheric Processes, Oceans and Ice Branch (josefino.c.comiso@nasa.gov)
- The most recent data in the Arctic reveal that for the third consecutive year, the perennial ice cover is anomalously low, especially in the Beaufort, Siberian, and Laptev Seas.  This has not happened before during the satellite era and it can be an indication that the ice-albedo feedback is already in full swing.  The perennial ice is ice that survives the summer melt and consists mainly of the thick multiyear ice cover that resides in the Arctic for as long as 7 years.  Because the albedo of open water is so much lower than that of sea ice, more extensive open water area in the summer means more heat is absorbed by the Arctic Ocean and this in turn means a warmer ocean.  A warmer ocean would mean earlier onset of melt, delayed onset of freeze-up and a relatively thinner ice that makes the ice cover even more vulnerable during the melt season.  A recovery of the multiyear ice cover is possible but it would require anomalously low surface temperatures for sustained periods during the Arctic summer.  Considering that there is global warming going on which is being amplified in the Arctic, this scenario is not likely in the near future.
- Paper presented at the IGARSS 2004 meeting in Anchorage, Alaska.

## Continuing decline in the Arctic Perennial Ice Cover

- 2004

- 1980

- 2002

- 2003

- Alaska

- Russia

## A Cold Land Processes Pathfinder (CLPP) Observing System Simulation Experiment (OSSE) Brian A. Cosgrove*, Paul R. Houser, Glen Liston*SAIC/NASA GSFC, Laboratory for Hydrospheric Processes, Hydrological Sciences Branch (brian.cosgrove@gsfc.nasa.gov)

** ****A snow/ice remote sensing mission (CLPP) is being planned to measure snow and ice characteristics at “process-scales”.**
** ****What is the optimal temporal and spatial resolution for this mission?**
** ****Which variables or active/passive microwave wavelengths are most important to observe?**
** ****Need to optimize benefit to land surface, weather, and climate models which will assimilate this data.**
**Through modeling and assimilation it may be possible to estimate process-scale snow characteristics using temporally thinned and spatially averaged data**

**A Cold Land Processes Pathfinder (CLPP) ****Observing System Simulation Experiment (OSSE)** **Brian A. Cosgrove*, Paul R. Houser, Glen Liston*****SAIC/NASA GSFC, Laboratory for Hydrospheric Processes, Hydrological Sciences Branch (brian.cosgrove@gsfc.nasa.gov)**

- Project Design:
**	****OSSEs have 4 central components *****(characteristics of this snow OSSE are stated in red)***** : **
*1. **	**“Truth” or*** ***nature run*** *****(Daily 30m SWE estimates for 30km x 30km area centered on Fraser, Colorado for the 2002-2003 winter based on Glen Liston’s model & data).***
*2.**	**Instrument simulator*** *****(Time/space sub-sampling of the 30m SWE estimates at 1-day, 3-day, 6-day, 9-day, 30-day repeat intervals, and 30m, 90m, 510m, 990m, 5010m resolutions)***
*3.**	**Data assimilation*** *****(Direct replacement method, with more advanced methods to follow)***** **
*4.**	**Experimental simulations*** *****(52 Noah and CLM2 LSM runs over a 6X6km ***_***representative***_*** sub-domain, for winter 2002-2003.  Objective function will be the 30m, daily SWE difference between the truth and experimental runs******. ***
**	**_****Iteration through these steps can be performed to optimize the instrument design**_

## Average CLM2 and Noah RMSE Values (mm) for study period

**Key Points:**	1) The no data assimilation (no DA) runs yield large RMSEs, especially in Noah 				LSM (this may be ameliorated with a recently released snow physics upgrade to 				Noah LSM)
- 			2) Daily assimilation of 510m data yields excellent results
- 			3) RMSEs are not uniformly distributed, and vary with geography (i.e. elevation)
- 			4) Noah results vary more with change in DA repeat time than do CLM2 results

- No Assimilation CLM2 Run

- 510m 1-Day Assimilation CLM2 Run

- 510m 9-Day Assimilation CLM2 Run

- No Assimilation Noah Run

- 510m 1-Day Assimilation Noah Run

- 510m 9-Day Assimilation Noah Run

## Area Averaged RMSE values (mm snow) for all Noah and CLM2 simulations

- All permutations of assimilation experiments are better than the no DA runs
- RMSE increases as repeat time increases, and as resolution decreases
- Shape of curves remain similar across model type and repeat time interval
- Repeat time curves are exponential in nature, with a break point between 90m and 510m

*Recommended observation resolution*:  Large benefit arises from any increase in observation resolution under 500m, so goal should be to fly as high a resolution instrument as possible
*Recommended repeat time* value is harder to determine, with steady decreases in RMSE as repeat time is decreased

## Model Output Comparison:

- CLM2 Versus Noah LSM

**Impact of Spatial Resolution**
- Curves are similar in shape between models and between data assimilation repeat times
- Noah features higher RMSEs than CLM2, sometimes performing worse than the CLM2 no DA run
- CLM2 simulations degrade more quickly with the coarseness of the observation data, than do Noah simulations
- Advantageous to use highest resolution data

**Impact of Observation Repeat Time**
- Curves differ greatly in shape between models
- Noah features higher RMSEs than CLM2, sometimes performing worse than CLM2 open run
- Noah simulations degrade much more quickly with the data assimilation repeat time than do CLM2 simulations
- Advantageous to use quick repeat times for Noah, less of a need for CLM2

## Brightness Temperature (TB) Comparisons:

Gail Skofronick-Jackson, NASA GSFC, Laboratory for Hydrospheric Processes, Microwave Sensors Branch (gail.s.jackson@nasa.gov)

In order to determine what happens to computed TB values for various common solid (snow) precipitation profiles, radiative transfer calculations were performed for a variety of situations.

Description:
Upwelling TB values at 89, 150, 183±1, ±3, ±7 were computed for profiles with clear air, heavy snow rate (~6” hr-1), medium snow rate (~1” hr-1), and low snow rate (~0.25” hr-1). Each profile was modified such that the lowest 1km of snow was melted (i.e., converted to rain) and separately to evaporate. Each profile had 35 layers. 

Figure and Table Captions:
Figure 1 shows the contributions for each of the four profile conditions. The curves can be integrated over height with the surface and cosmic background (CB) contributions added to obtain the TB for each channel. 
Figure 2 indicates the TB contribution amount from snow alone and from water vapor (relative humidity) alone. 
Table 1 presents the TB values for various cloud situations, while Table 2 shows the percentage of the TB for 89, 150, and 183±7 GHz that comes from the surface, the snow particles, and from water vapor. The CB and other atmospheric contributions (e.g., oxygen) add to sum to 100%.

Results:
Comparing to clear air TB values (Table 1), the heavy snow radiative transfer calculations produced TB values that were colder by nearly 60 Kelvin, the medium snow rate case TB values were about 8 K cooler, while the low snow rate case had TB differences around 4 K. The TB changes were most significant at 89, 150, and 183±7 GHz.
The contributions for the various cases change the peak height and curve width in Figure 1, especially for 183±7 GHz. The probing nature of these channels is apparent. 
When melting occurs in the lowest 1km of the profile, TB are warmed at 89 and 150 GHz by about 5-8 K for the heavy case and by about 3-4 K for the low snow case. For evaporation (virga) near the Earth’s surface, the 89 and 150 GHz TB values are warmed only minimally (4 K for the heavy case and 2 K for the low snow case).
For both evaporation and virga, no TB changes were seen at the 183 GHz channels because these channels do not see down to the Earth’s surface due to their high sensitivity to water vapor that causes saturation.
Note that the 183 GHz channels get the majority of their signal from the water vapor in the profile, whereas 89 and 150 get slightly more of their signal from the snow particles in the cloud profile. (Figure 2 and Table 2)

Conclusions:
It is likely that only heavy and possibly medium snow profiles can be retrieved. Unknowns about the surface and snow cause TB  variations on the order of the TB changes for low snow rates. 
It will be difficult to determine if melting or evaporation near the surface occurs using these channels alone. 
Additional studies for more channels and for melting or virga starting at 2-3 km above the surface are underway. 
A detailed field campaign and/or in situ study is needed for validation purposes.

**Gail Skofronick-Jackson, NASA GSFC, Laboratory for Hydrospheric Processes, Microwave Sensors Branch (gail.s.jackson@nasa.gov)**
**In order to determine what happens to computed T****B**** values for various common solid (snow) precipitation profiles, radiative transfer calculations were performed for a variety of situations.**
**Description:**
**Upwelling T****B**** values at 89, 150, 183****±****1, ****±****3, ****±****7 were computed for profiles with clear air, heavy snow rate (~6” hr****-1****), medium snow rate (~1” hr****-1****), and low snow rate (~0.25” hr****-1****). Each profile was modified such that the lowest 1km of snow was melted (i.e., converted to rain) and separately to evaporate. Each profile had 35 layers. **
**Figure and Table Captions:**
**Figure 1**** shows the contributions for each of the four profile conditions. The curves can be integrated over height with the surface and cosmic background (CB) contributions added to obtain the T****B**** for each channel. **
**Figure 2**** indicates the T****B**** contribution amount from snow alone and from water vapor (relative humidity) alone. **
**Table 1**** presents the T****B**** values for various cloud situations, while ****Table 2**** shows the percentage of the T****B**** for 89, 150, and 183****±7 GHz that comes from the surface, the snow particles, and from water vapor. The CB and other atmospheric contributions (e.g., oxygen) add to sum to 100%.**
**Results:**
**Comparing to clear air T****B**** values (Table 1), the heavy snow radiative transfer calculations produced T****B**** values that were colder by nearly 60 Kelvin, the medium snow rate case T****B**** values were about 8 K cooler, while the low snow rate case had T****B**** differences around 4 K. The T****B**** changes were most significant at 89, 150, and 183****±7 GHz.**
**The contributions for the various cases change the peak height and curve width in Figure 1, especially for 183****±7 GHz. ****The probing nature of these channels is apparent.**** **
**When melting occurs in the lowest 1km of the profile, T****B**** are warmed at 89 and 150 GHz by about 5-8 K for the heavy case and by about 3-4 K for the low snow case. For evaporation (virga) near the Earth’s surface, the 89 and 150 GHz T****B**** values are warmed only minimally (4 K for the heavy case and 2 K for the low snow case).**
**For both evaporation and virga, no T****B**** changes were seen at the 183 GHz channels because these channels do not see down to the Earth’s surface due to their high sensitivity to water vapor that causes saturation.**
**Note that the 183 GHz channels get the majority of their signal from the water vapor in the profile, whereas 89 and 150 get slightly more of their signal from the snow particles in the cloud profile. (Figure 2 and Table 2)**
**Conclusions:**
**It is likely that only heavy and possibly medium snow profiles can be retrieved. Unknowns about the surface and snow cause T****B****  variations on the order of the T****B**** changes for low snow rates. **
**It will be difficult to determine if melting or evaporation near the surface occurs using these channels *****alone*****. **
**Additional studies for more channels and for melting or virga starting at 2-3 km above the surface are underway. **
**A detailed field campaign and/or in situ study is needed for validation purposes.**

## Brightness Temperature Sensitivity 

***Dr. Gail Skofronick Jackson, Code 975***

**Brightness Temperature Sensitivity **
**to Changes in Solid (Snow) Precipitation Profiles***** ***

**Amount**
**from**
**Snow**

**Amount**
**from RH**

| Channel
(GHz) | Clear Air | Heavy | Heavy: 
Melt | Heavy:
Evap. | Medium | Low | Low: 
Melt |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 89 | 212.3 | 194.5 | 212.6 | 199.5 | 210.4 | 211.3 | 215.4 |
| 150 | 243.4 | 185.3 | 190.9 | 189.5 | 235.8 | 240.2 | 243.3 |
| 183±1 | 240.9 | 235.6 | 235.6 | 235.6 | 240.0 | 236.6 | 236.6 |
| 183±3 | 251.1 | 235.5 | 235.5 | 235.5 | 247.3 | 249.3 | 249.3 |
| 183±7 | 260.4 | 220.5 | 220.5 | 220.5 | 253.9 | 256.8 | 256.8 |

***Integrate curves & add surface & cosmic background for T******B****** values 89, ******150******, ******183******±******1******, ******±******3******, ******±******7 ******GHz)***

**Table 1: Brightness temperature values (K)**

**Clear Air**

**Heavy **
**Snow**

**Medium **
**Snow**

**Light**
**Snow**

**Height**

**Figure 1: Four profile cases (*****89, ******150******, ******183±1******, ******±3******, ******±7 ******GHz)***

**Figure 2: Contributions**

| Case | % from surface
89, 150, 183±7 | % from snow
89, 150, ±7 | % from RH
89, 150, ±7 |
| --- | --- | --- | --- |
| Clear Air | 74, 53, 5% | 0, 0, 0 | 19, 44, 94 |
| Heavy | 56,14, 9 | 35, 77, 72 | 6, 8, 26 |
| Medium | 71, 43, 3 | 9, 30, 24 | 14, 25, 72 |
| Light Snow | 72, 47, 4 | 6, 20, 14 | 16, 31, 81 |

**Table 2: Percentage contributions**