# Presentation: 809414

## Slide 1

## Tech Review Agenda

-             Topic		   Speaker	     Time

- Overview/Introduction:	Seth Gutman	1330 - 1345			FSL/DD
- GPS Satellite Orbits:	Peng Fang	1345 - 1400	                     	SIO/SOPAC
- NWP Model Studies: 	Tracy Smith	1400 – 1415			FSL/FRD CIRA
- Break 					1415 - 1430
- Data Products & Services:	Susan Sahm           1430 - 1445  			FSL/DD
- Data Applications:		Daphne Grant	1445 - 1500 			FSL/DD
- Operational Transition: 	Kirk Holub	1500 - 1515			FSL/DD
- Concluding Remarks:	Seth Gutman	1515 – 1520
- Questions:				1520 – 1530

## Overview/Introduction

- The GPS-Met project started in 1993 as a collaboration between FSL, UCAR and NCSU to determine if & how GPS could be used to measure atmospheric moisture.
- It has evolved into a collaboration between FSL, other NOAA organizations, other federal, state and local government agencies, universities, and the private sector.
- This level of cooperation has permitted us to develop, test and evaluate a new upper-air observing system for less than 10% of the Demonstration Division’s budget.
- Major accomplishments include: specification of the observation accuracy and error covariance; co-development of real-time data processing techniques; verification of positive impact on Wx forecast accuracy; definition and exploration of new applications.

## Collaborations

- FSL 			FRD, TOD, AD, SDD, MD, A&R, ITS
- NOAA Research	ETL, AL, AMOL, CMDL, PMEL, GLERL, SEC
- Other NOAA:	NWS (NDBC, ER, SR, CR, WR, AR, NCEP), NOS (NGS, CO-OPS), NESDIS (ORA), NGDC, NCDC
- Federal Gov’t:	DOT (FHWA), DHS (USCG), DOD (USN, USAF, USACE), NASA (LaRC, JPL, GFSC), DOE (ARM)
- Universities: 		SIO, UH, UCAR, MIT, H-SAO, OSU, Purdue, U. Calgary, USM, CU, CSU, LSU, and SuomiNet
- Other Gov’t:		AZ (various), AKDOT, CO (various), FDOT, MDOT, MNDOT, OHDOT, OKDOT, NYDOT, NCDOT, TXDOT, VTDOT

## Supporting the NOAA Strategic Plan

- GPS-Met observations contribute to 3 of 4 NOAA Mission Goals:					           -	Understanding climate variability and change to 		enhance society’s ability to plan and respond;            -	Serving society’s needs for weather and water 		information;					              -	Supporting the Nation’s commerce with 			information for safe, efficient, and 				environmentally sound transportation.
- GPS-Met also contributes to 4 of 6 cross-cutting priorities essential to support NOAA mission goals:	-	integrated global environmental observation and 		data management systems;			              -	sound, reliable state-of-the-art research;	         -	international cooperation and collaboration; and             -	homeland security.

## Supporting the FSL Strategic Plan

- FSL conducts applied meteorological R&D to create and improve short-term warning and weather forecast systems, models, and observing technologies.
- Ground-based GPS-Met contributes to all of these activities by providing high accuracy moisture observations under all weather conditions to forecasters, modelers, and researchers.
- The unique capabilities of FSL have enabled the GPS-Met observing system to be developed, tested, and validated in a relatively short period.
- Positive impact on Wx forecast accuracy has been demonstrated and verified using the FSL-developed RUC.
- Lessons learned from using FX-Net will be applied to AWIPS, as we build GPS-Met display applications for WFOs in collaboration with SDD and MD.

## Technology Transfer/Outreach

- FSL transfers new scientific and technological advances to its clients, including the National Weather Service, Department of Defense, foreign weather forecasting agencies, and private interests.
- To facilitate this, DD funded a modest outreach activity led by Sher Schranz of FSL/TOD and CIRA, with assistance from Rhonda Lange, Will von Dauster and John Osborne.
- Joe Golden also provided assistance, especially with forecast offices.
- Efforts concentrated on NWS, FHWA, and DOD users.
- Ongoing cooperation with Patty Miller and the MADIS group has greatly expanded access to GPS-Met data and products.

## Since 2002 Tech Review

- The original system architecture (GPS + collocated Sfc. Met + dedicated comms) was modified to enable us to use GPS sites without all of these attributes.
- We created the backbone site vs. infill site distinction.
- The number of sites in the network increased from 121 to 291.
- The number of systems in the processing array went from 12 to 18.
- Latency (mean time from the end of a 30-min session until PW is delivered) decreased from 20 minutes to 14 minutes.
- The number of GPS sites within 50 km of an NWS UA site increased from 4 to 48.

## History, Evolution & Critical Decisions

**History, Evolution & Critical Decisions**

- 336 hrs
- Precise Orbit

- 36 hrs
- Rapid Orbit

- 0.3 hr
- Hourly Orbit

- Data Processing Latency

- Arbitrary

- SuomiNet

## GPS-Met Demonstration Network

- 121 GPS-Met Sites + 52 waiting for positions

## Since 2002 Tech Review

- NASA Co-PI on Aqua/AIRS science team.  Provided calibration-validation PW data to all investigators.	-	Participating in AIRS cal/val experiments with 		NESDIS/ORA.

**Since 2002 Tech Review**

**Notes:**
Improving Moisture Soundings from Satellite Remote Sensors
Using GPS-Met to remove raob bias in first-guess for PW retrievals shows substantial promise.

## Since 2002 Tech Review

- Participated in IHOP 2002.  Provided real-time data and analysis to all investigators.				-	In collaboration with researchers at NESDIS and 		CIMMS, Dan Birkenheuer and I are studying the 		temporal observation error structure of GOES-8 		PW retrievals during IHOP by comparing them 		with GPS and sondes at synoptic and	asynoptic 		times.

**Since 2002 Tech Review**

## Since 2002 Tech Review

**Since 2002 Tech Review**

## Since 2002 Tech Review

**Since 2002 Tech Review**

**UNB3 – WAAS Predictor GPS Estimation – DRV1**

**Peng Fang**
**Scripps Orbit and Permanent Array Center (SOPAC)**
**University of California San Diego**
**January 27, 2004**

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing System    **
**2004 Technical Review**
**Impact of Imperfect Orbits on****Ground-Based GPS Atmospheric Sensing Application**

## Outline

**SOPAC Hourly Orbits**
**Factors Affecting Orbit Performance**
**Global Network Configuration**
**Observation Span and Data latency**
**Satellite Performance**
**Auxiliary Information**
**Reliability of Operational Facilities**
**Impact upon GPS/MET Applications**
**Solutions to Various Problems**

## SOPAC Hourly Orbits

- Sliding window technique
- 24 hour data span (reason: maintain orbit continuity by fitting longer arc)
- Rejecting under performing satellites using internal and external checking
- Separate full constellation processing for resuming previously rejected satellites
- Duplicated data acquisition from multiple sources

## SOPAC

**Full Constellation Hourly**

** ****Orbits QC**

** ****Net 1**

***SOPAC***

***1h***

***FSL***

***24 h***

**Internet**

** **

** ****Aux. Data**

** ****IGS Hourly Data**

** ****Net 2**

** ****Net N**

** **

** **

** **

***8h***

***30m***

***Orbits with***
***12h prediction***

**Data Collect =< 1h   Proc.Time = 1h  Application uses 2+h prediction**

## Global Network Configuration

- When tracking network has large gaps (network holes), there will be very little or no data to fit the orbits over the paths above them. Thus the orbital characteristics could not be modeled well, resulting in poor prediction.

## IGS hourly sites of global tracking network

- Blue = to be used

- Red = Possible to use

- Green = temporarily unavailable

**IGS hourly sites of global tracking network**

## Observation Span and Data latency

- Longer span helps fitting longer orbital arc which in turn helps the orbit prediction. However the processing burden increases. One key parameter, “once per rev.”, could not be estimated well with short observation span.
- Higher latency means effective processing data span decreased.

## Satellite Performance

- Satellite reposition
- Misbehaving satellites
- Eclipsing
- Reset
- Higher general noise level

## Auxiliary Information

- Earth Orientation Parameters (polar motion, UT) e.g. poor prediction, missing update -> biased orbits -> biased tropo. delay estimates. e.g. over shoot at bending
- Global reference frame  e.g. Earthquake or site configuration change (antenna/receiver/monument) on tightly constrained sites -> error goes into orbit

## Reliability of Operational Facilities

- Hardware failure (most often: RAM, hard drive, power supply)
- Data server overloading (shared scripts, executables, auxiliary files)
- Processing node overloading (usually after network interruption)
- Intranet interruption (e.g. DNS down)
- Internet interruption (e.g. maintenance, unusual event)

## Impact upon GPS/MET Applications

- Poor performing satellite included
- Poor configuration of global tracking network used
- Poor latency of data supply (less data to fit orbit, predicted orbit would not be good)
- Late orbit delivery (not to show) note: this is different from previous point. Using predicted orbits up to 8 hours should be OK

## Experiment Setup

- Reference set: IGS final orbits
- GW1247 PRN24 (336) PRN31 (338,339)
- Reduced satellite from 27/28 to 22
- Removed last 6 hour observation
- Excluded 5 sites usually having latency problem
- Total number of solutions: 24x8x5x2

## Slide 27

## Slide 28

## Slide 29

## Slide 30

## Solutions to Various Problems

- System redundancy
- Increased session span
- Independent check
- Improvement in QC procedures
**Reject satellite to be repositioned in advance**
**Have alternative orbits ready**

## NOAA Forecast Systems Laboratory

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing System    **
**2004 Technical Review**
**Ongoing Assessment of GPS-IPW  Impact on RUC Forecasts **

**Tracy Lorraine Smith                                                              ****Forecast Research Division                                   **
**January 27, 2004**

-  RUC experiments for GPS impact
**60km RUC**
- 1998 – 2003
- Ongoing 3h cycles with and without GPS IPW assimilation
**20km RUC**
- 5-day experiment – May 2000
- 15-day experiment- February 2001
- Ongoing 1h assimilation cycles with and without GPS IPW assimilation, comparisons and statistics available
-   http://waylon.fsl.noaa.gov/cgi-bin/ruc20/ruc20.cgi

## NOAA/FSL GPS network

- Current accuracy - IPW
- 	- RMS error 		<1.5 mm
- 	- bias			<0.25 mm  (positive)
- Latency – 	18 min, use ‘hourly orbit’ – from RUC requirement
- 	00-30 min avg, available at +48 min

- Information from
- Seth Gutman,
- Seth.I.Gutman@noaa.gov

- http://gpsmet.noaa.gov

-   291 stations

## Hourly Data for RUC60/RUC20

- Data Type			~Number	Frequency
- Rawinsonde (balloons)	       		80		/12h
- NOAA 404 MHz wind profilers   		31		/  1h
- PBL (915 MHz) wind profilers    		24		/  1h
- RASS virtual temperatures	       	10		/  1h
- VAD winds (WSR-88D radars) 	    110-130		/  1h
- Aircraft (ACARS)	              1400-4500		/  1h
- Surface/METAR		              1500-1700		/  1h
- Surface/Buoy			    100-150		/  1h
- Surface/Mesonet		  2500-4000		/  1h
- GOES cloud-drift winds	  	  1000-2500		/  1h
- GOES precipitable water	   	  1500-3000		/  1h
- GPS precipitable water	     	    278			/  1h
- SSM/I precipitable water      	  1000-4000		/  6h
- GOES cloud-top pressure/temp    10km res		/  1h
- Ship reports/dropsondes			as available

- Much competing data for GPS-IPW over US

## Optimal interpolation analysis for precipitable water obs

-    - 2-d analysis of PW (ob – bkg)
-    - percentage correction applied to water vapor mixing ratio
- 	at all levels
- Unavoidable problem – aliasing, esp. vert

- PW ob errors
-  GPS		1 mm
-  GOES	3 mm
- 1h forecast error
- 		5 mm

- RUC20 PW changes
- Account for station
-  vs. model terrain
-  difference
- - no change
-     above 500 hPa
- iterated solution
-  w/ PW, cloud,
-  in situ analysis

## Conclusions – RUC60 GPS impact tests

- Multi-year study with the 60km RUC indicates that GPS-Met makes a small but consistent positive impact on short-term weather forecast accuracy:
- 	primarily at the lower levels where most of the moisture resides    	 *- *IPW more correlated w/* low-*level moisture
- magnitude of impact consistently increases with the number of stations
- RH forecast improvement is greatest in the cool months when convection is less frequent and the moisture distribution is more synoptic scale.
- impact on precipitation forecast accuracy generally increases with precipitation amount threshold

- No. Sta		18	56	67	100+	200+
- Level	    **1998-99****	****2000****	****2001****	****2002****	****2003**
- 			% improvement (normalized by total error)
-  850			1.5	3.8	3.9	5.0	5.4
-  700			1.1	4.1	6.3	6.5	7.0
-  500			0.7	2.1	2.0	2.4	3.1
-  400			0.3	0.1	-0.4	-0.5	1.0
- Mean (850-400)	0.9	2.5	2.9	3.3	4.1
- Mean (850-500)	1.1	3.3	4.1	4.6	5.2

- Verification area

## Impact of GPS-IPW increases as the number of GPS observations increase

- Largest impact at 700 and  850 hPa, lower troposphere

## At 850 hPa there is a definite seasonal modality on the magnitude of the impact not seen at 700 hPa

- Monthly variation of GPS impact on 3h RH forecasts

## Impact of GPS on 3h RH forecasts verified against RAOBS at 00 and 12 UTC 01 Jan 03 - 31 Dec 03

- Run by run verification shows impact can vary widely.
- Impact is greatly affected by weather regime.

-  RUC experiments for GPS impact
**60km RUC**
- 1998 – 2003
- Ongoing 3h cycles with and without GPS IPW assimilation
**20km RUC**
- 5-day experiment – May 2000
- 15-day experiment- February 2001
- Ongoing 1h assimilation cycles with and without GPS IPW assimilation, comparisons and statistics available
-   http://waylon.fsl.noaa.gov/cgi-bin/ruc20/ruc20.cgi

## IPW differences between 20km RUC analyses and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003

**With GPS**   RUC-GPS   RMS
- Number        2135	       2135
- Mean (mm)  0.25	       2.36

**Without GPS  **RUC-GPS  RMS
- Number 	  2103		2103
- Mean (mm)	  1.16		3.94

- Bias

- RMS

## IPW differences between 20km RUC 3h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003

**With GPS**   RUC-GPS   RMS
- Number        2131	       2131
- Mean (mm)   0.13	       3.07

**Without GPS**  RUC-GPS  RMS
- Number	    2131         2089
- Mean (mm)	    3.07          3.92

- Bias

- RMS

## IPW differences between 20km RUC 6h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003

**With GPS**   RUC-GPS   RMS
- Number        2131	       2131
- Mean (mm)  -0.06	       3.40

**Without GPS**  RUC-GPS	RMS
- Number	   696		696
- Mean (mm)	   0.62		3.84

- Bias

- RMS

## IPW differences between 20km RUC 9h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003

**With GPS**   RUC-GPS   RMS
- Number	2131	       2131
- Mean (mm)	-0.30	       3.65

**Without GPS**  RUC-GPS  RMS
- Number 	  697		697
- Mean (mm)	  0.33		3.82

- Bias

- RMS

## IPW differences between 20km RUC 12h forecasts and GPS-IPW obs at ~225 sites for 25 Jul - 22 Oct 2003

**With GPS**   RUC-GPS   RMS
- Number        709	       709
- Mean (mm)  -0.48	       3.91

**Without GPS**  RUC-GPS	RMS
- Number	   696		696
- Mean (mm) 	   0.03		3.89

- Bias

- RMS

## Difference RUC20 analysis with GPS minus RUC20 without GPS 9 Nov 2003 1500 UTC

## Time series of RUC IPW, GPS IPW, and RAOB IPW at Jacksonville, FL for 6 - 11 November 2003

- FSL RUC with cold start from ETA
- is too dry, GPS-IPW can correct

## Bias and RMS for 20km RUC with and without GPS-IPW for 6-11 Nov 2003

- Cold start for FSL RUC with GPS, GPS-IPW helps RUC to
- recover from dry bias, high RMS error 9 hrs before RAOBs

- + RUC without GPS-IPW

- + RUC with GPS-IPW

## Conclusions from RUC20 GPS impact studies

-  Impact on 3h RH forecasts similar to that from RUC60
-  IPW forecast improvement evident out to 9 h
-  Interactive, ongoing assessment of GPS impact is
-  enhanced by the GPS/model comparison webpage
- _Future_
- Multi-week RUC20 retrospective impact tests
- Assimilation into operational RUC20 at NCEP

- http://ruc.fsl.noaa.gov
- All FSL RUC forecasts (out to 48h) initialized with GPS-IPW

## NOAA Forecast Systems Laboratory

**Susan Sahm                                                              ****GPS-Met Observing Systems Branch                                   **
**January 27, 2004**

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing System    **
**2004 Technical Review**
**GPS Data Products and Services **

**Notes:**
I’ll be talking about the GPS products and services

## New GPS-Met Products & Services 

- In collaboration with FSL/FRD*, we developed a web-based tool to assist modelers and researchers to compare and evaluate IPW derived from GPS, rawinsondes, and NWP models.
- In response to a request from Ryan Jewell, a forecaster at the Storm Prediction Center (SPC) in Norman, OK, we can now produce experimental 1-h, 2-h, and 3-h precipitable water vapor-change products every hour.
- With modest funding from the Interagency GPS Executive Board (IGEB),** we evaluated the feasibility of using NWP models to produce tropospheric signal delay correctors for high accuracy NDGPS positioning and navigation.

- *   Our thanks to Stan Benjamin, Tracy Smith, Kevin Brundage, and Bill Moninger
- ** Our thanks to Jim Arnold - FHWA Technical Point of Contact

**Notes:**
There are 4 projects I am going to briefly describe today:
We worked with FRD, Stan, Tracy, Kev and Bill Moninger to create a web tool to  compare GPS IPW with raobs and RUC20 model runs
read
This was done for the USCG and US Dept of Transportation

## New GPS-Met Products & Services 

- We are collaborating with FSL/SDD* and the SOOs at several WFOs to prototype D2D applications using existing depictables.
- We are working with NWS and SDD to include many of these capabilities in the next AWIPS build along with the new wind profiler and RASS products.
- On 01/07/04, David Helms (OST Science Plans Branch) recommended GPS-IPW displays be included in AWIPS OB5.

**New GPS-Met Products & Services **

- *Thanks to Herb Grote, Susan Williams, and Patty Miller

**Notes:**
The last project I’ll describe are GPS prototypes for  AWIPS.
OST (Office of Science and Technology)

## Wx Models & Satellite Images Web Site

**Wx Models & Satellite Images Web Site**

**Notes:**
Our web based tool to compare NWP models w/ and w/out GPS can be accessed from the GPS home page,  choose Wx models....

## Wx Models & Satellite Images Web Site

**Wx Models & Satellite Images Web Site**

**Notes:**
From this page, you can access different types of plots:  
Contours of RUC model runs with or without GPS for Analysis and 1 or 3 hr forecasts
Contour Plots showing differences between RUC with and w/out GPS 
CONUS maps showing the difference between RUC w/ and w/out GPS at each site
Difference contour plots of Model forecast and analysis runs
GPS observations at each site overlaid on satellite images.
Inputs to the FSL RUC20
These plots can also be animated

## Wx Models & Satellite Images Web Site

**Wx Models & Satellite Images Web Site**

**Notes:**
RUC 20 PW analysis with a PW contour interval of 5 mm.  The black dots show the locations of the GPS-Met sites (currently 271) in the CONUS region.
Statistics on the bottom of the plot show the mean and RMS differences between the GPS observations and RUC model values averaged over CONUS
Also tell number of comparisons and number of inputs to the model
Mousing over any of the sites will display the GPS values, and model values, if applicable

Conus map showing inputs to the FSL RUC.  The green indicates where there were inputs and red is missing.  Mousing over displays the input values.

## Wx Models & Satellite Images Web Site

**Wx Models & Satellite Images Web Site**

**Notes:**
LEFT - Difference contour between the RUC w/ and without GPS.  Shows stats for both models.
RIGHT – 
shows difference between RUC w/ and w/out at each site.   
The color denotes the difference in mm for the Model value that is the closest to the GPS observation. 
Diamonds are where FSL is closer, Stars where NCEP closer, box – same, + missing data.  
Stats for both are at bottom

## An Unanticipated Application

**IAD**

**An Unanticipated Application**

**Notes:**
An unanticipated application of this tool is that it can be used to identify potential problems with rawinsonde moisture soundings.
This is accomplished by using the version of the RUC 20 with GPS to plot the difference between the one-hour forecast valid at a synoptic time (0Z or 12Z) and the analysis at that time.  In general, we have found that the model “reaches” for the sounding and returns (usually quickly) to the GPS-constrained value.  The impact of these events is usually obvious on the contour plot of the differences.
The next slide illustrates the most probable cause of this behavior: a bad moisture sensor on the sonde.

## Slide 59

**Notes:**
According to Carl Bower (NWS), the tendency for RH to remain high as the balloon passes through the tropopause (identified by the reversal in the temperature lapse rate – the temperature begins to increase with height) is an indication of a problematic sensor.

## Statistical Analysis Using the Web Tool

- Statistical comparison of RUC 20 model analysis (with and without GPS IPW retrievals) for the 90 day period from 25 July to 22 October, 2003.

- RMS

- Bias

**Notes:**
These histograms show the statistical comparison of the RUC20 with and w/out GPS
The GPS slightly moistens the RUC analysis and reduces the differences between the GPS and the Model  
The 1-hr fcst is virtually identical to the analysis
This trend continues through the 3-hr Fcst
Less impact after 3 hr fcst and the scatter for the RUC with GPS has the same characteristics as the model without
More samples of FSL Ruc20 for the 6 and 12 hour fcst

## SPC Water Vapor Change Maps

- High temporal frequency GPS moisture observations improve overall situational awareness, and this almost always makes a positive impact on forecast services during active weather.
- GPS moisture observations have the potential to improve warning lead times during emergency situations like flash flood events, but this has not yet been verified.

**SPC Water Vapor Change Maps**

**Notes:**
Another project we worked on was the water vapor change plots.

## SPC Water Vapor Change Maps

- Water vapor time change fields (1, 2, and 3-hr) can give the forecaster an idea of where moisture is converging. This helps infer where the moist boundary layer is deepening with time and where the first storms are likely to form.

**SPC Water Vapor Change Maps**

**Notes:**
Storm Prediction Center

## SPC Water Vapor Change Maps

- To assist SPC in evaluating a PW change product, we produced maps of 1, 2, and 3-h PW change at GPS-Met sites overlain on GOES WV images.

**Notes:**
Change in GPS observations.  
The black dots are missing data

## SPC Water Vapor Change Maps

- We also created 1, 2, and 3-h PW change contour maps using the FSL version of the RUC 20.

## SPC Water Vapor Change Maps

- The approach that SPC is considering for the short-term is to ingest point data directly from FSL to display on their N-AWIPS work stations themselves.
- SPC would prefer to work with ***observations*** rather than ***products***, and they have asked us to move toward a denser observing network with more uniform geographic coverage.

## HA-NDGPS Project

- To achieve the real-time accuracy target of < 20 cm, ***signal delays caused by the ionosphere and troposphere must be taken into account***.  Modeling is one way to do this.
- Agencies involved in the study are:
- FSL – nowcasting tropospheric delays;
- Space Environment Center (SEC) – nowcasting ionospheric delays;
- National Geodetic Survey (NGS) – describing/ articulating ionospheric & tropospheric correctors;

**HA-NDGPS Project**

**Notes:**
The High Accuracy NDGPS project was a new project for the GPS-MET group last FY.

## Atmospheric Signal Delay Structure

- Tropospheric Terms

**Notes:**
Total atmospheric refractivity has an ionospheric and neutral atmospheric component usually referred to as the tropospheric delay.  This is because most of the variability in the neutral delay comes from water vapor in the troposphere.
The tropospheric delay has two components: a dry component that is proportional to surface pressure, and a wet component proportional to the number of water vapor molecules along the paths of the GPS signals.

## HA-NDGPS Project

- Every hour, we compute estimates of dry refractivity (derived from model pressure at a constant geopotential height) and wet refractivity (derived from condensation pressure converted to mixing ratio, and integrated from the model surface elevation to the modeled height of the tropopause.)
- We developed a tool kit to compute ZTD from wet and dry refractivity at any location in the model domain site using these grids and user specified site parameters.

**Notes:**
These values are stored in grids for the northeastern US
User inputs site lat, lon, elevation, and time
Evaluations are in progress

## HA-NDGPS Project                                           Sample Contour Files

**HA-NDGPS Project                                           Sample Contour Files**

- This product is being evaluated by U. Calgary, U. Southern Mississippi (USM), and contractors to the USCG and the FHWA; no results are available to date.

**Notes:**
Contour plots of ZHD and ZWD at 11 UT Analysis and Forecast valid at 13 UT.
These grid values would be Interpolated to compute “realtime” values at 12 UT.

## D2D Prototypes

- We will continue to work with AWIPS users to determine requirements for the GPS D2D displays.
- We will work with FSL/SDD to develop displays using existing depictables.
- Regular meetings will be held with SDD to relay user feedback.
- When the requirements have been compiled, SDD personnel will determine which displays may be incorporated into the next AWIPS workstation build.

**Notes:**
The last project I’m going to talk about is the D2D prototypes.

## D2D Prototype Displays                              Under Consideration

- Model derived PW
- ETA, RUC, GFS
- Data Merges
- GPS-IPW + satellite images
- GPS-IPW +WSR-88D amplitude products
- GPS-IPW +WSR-88D precip products
- GPS-IPW + lightning
- GPS-IPW + NPN profiler wind barbs at various levels.
- PW site climatology.  Might take the form of monthly average PW and range.  Presented as point of comparison for current value.

**Notes:**
Here is a list of some possible prototypes for AWIPS displays.

## NOAA Forecast Systems Laboratory

**Daphne Grant**                                                                 **NPN Operations Branch**
**January 27, 2004**

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing System    **
**2004 Technical Review**
**GPS Data Applications **

**Notes:**
I’ll be talking about the products and services and Daphne will talk about Forecaster applications

## GPS-Met Applications

- DD runs an application every day that finds all area forecast discussions (AFD’s) with references to GPS or IPW.
- Each reference was categorized by:
- forecast office;
- date & time;
- GPS site ID or region;
- how GPS data were used (e.g. comparisons to 		models, satellite, ACARS, RAOBS, etc.)
- After analyzing the results, forecasters at the 6 WFOs most often discussing GPS were asked to answer some questions about how they were using these data.

## Number GPS Discussions By WFO

| NWS Office | Refs | Rank |
| --- | --- | --- |
| Baltimore MD/Washington D.C. | 1 | 11 |
| Binghamton, NY | 1 | 11 |
| Blacksburg, VA | 1 | 11 |
| Cleveland, OH | 1 | 11 |
| Denver, CO | 1 | 11 |
| Duluth, MN | 1 | 11 |
| Lake Charles, LA | 1 | 11 |
| Lincoln, IL | 1 | 11 |
| Nashville, TN | 1 | 11 |
| New Orleans/Baton Rouge, LA | 1 | 11 |
| Pittsburgh, PA | 1 | 11 |
| St. Louis, MO | 1 | 11 |
| Tucson, AZ | 1 | 11 |
| Upton, NY | 1 | 11 |

| NWS Office | Refs | Rank |
| --- | --- | --- |
| Flagstaff, AZ | 30 | 1 |
| Miami, FL | 25 | 2 |
| Burlington, VT | 24 | 3 |
| Houston/Galveston, TX | 19 | 4 |
| Jacksonville, FL | 11 | 5 |
| Key West, FL | 10 | 6 |
| Phoenix, AZ | 6 | 7 |
| Salt Lake City, UT | 5 | 8 |
| Chicago, IL | 4 | 9 |
| Melbourne, FL | 4 | 9 |
| Jackson, KY | 2 | 10 |
| La Crosse, WI | 2 | 10 |
| Morristown, TN | 2 | 10 |
| Ruskin, FL | 2 | 10 |

**Number GPS Discussions By WFO**

- 28 WFO’s included GPS in their forecast discussions during 2003.

## Total No. Discussions/Month 

**Total No. Discussions/Month **

**Number of AFDs**

**Months (2003)**

## Discussions/Month By WFO

**Discussions/Month By WFO**

**Number of AFDs**

**Months (2003)**

## GPS Discussions

- GPS IPW retrievals were compared with NWP models 60 times: GPS verified the model 44 times and contradicted it 16 times.
- The GPS data were compared to raobs soundings 54 times: 46 times they agreed and 8 times they disagreed.
- The GPS data by itself was the subject of discussion 53 times.
- The GPS data were compared with satellites 29 times: 24 times it verified the interpretation and 5 times it contradicted the interpretation.
- The GPS data were compared to surface dewpoints 3 times.
- GPS data were compared to ACARS data 3 times: they agreed 2 times and disagreed once.

## Forecast Applications Survey

- We identified the top 6 reporting WFOs: Flagstaff, Miami, Burlington, Houston/Galveston, Jacksonville, and Key West.
- We e-mailed a survey to forecasters at these offices asking 8 questions and 5 responded.
- Questions:
- Considering the GPS data products and services provided, how are we (the GPS-Met  Branch) doing?
- What are the strengths of the data?
- What are the weaknesses of the data?
- When is the data the most useful?
- When is the data the least useful?
- What can we do better? / what can we improve upon?
- When looking at soundings, do you look at RAOB soundings or satellite soundings?
- Do you have any questions or additional comments?

## Survey Results

- Most of the forecasters agreed that the GPS-Met branch is providing good access to real-time data.
- “doing a great job of providing data in real- 		time...”
- “web interface easy to use to get data.”
- “The data is fairly robust...”

**Considering the GPS data products and services provided, how are we (the GPS-Met  Branch) doing?**

## Survey Results

- The primary response was accuracy and temporal data frequency.  People also liked the following features:
- being able to plot multiple sites along with 		raob data;
- the GPS sites are spatially laid out;
- “helps to validate (or invalidate) NWP 			forecasts.”

**2)****	****What are the strengths of the data?**

## Survey Results

- “...its latency arriving into AWIPS.”
- ... there isn’t an IPWV climatology.
- The Burlington, VT forecaster discovered that 		one nearby site, Hudson Falls, NY, had a 		consistent wet bias.
- Bill Murray wrote, “The IPW data frequently 		looks to be a tad high on it’s reading of PW 		when compared to RUC/ETA/GFS/NGM PW 		data.”

**3)****	****What are the weaknesses of the data?**

**Survey Results**

## Oops!                 Wrong ASOS.

## In Flagstaff, the data are most useful during the summer monsoon season and during the cold season with the approach of a trough or low pressure system.
In Florida, the data was most useful for the
Convective season
Frontal passages
Tropical systems/cyclones/surges/waves
In Burlington, GPS data are most useful when they are expecting precipitation or severe weather.

- In Florida, the data was most useful for the
- Convective season
- Frontal passages
- Tropical systems/cyclones/surges/waves
- In Burlington, GPS data are most useful when they are expecting precipitation or severe weather.

**4)****	****When is the data the most useful?**

**Survey Results**

## All responses were the same... ”it’s the least useful when there is quiet weather.”

The responses were:
“...more flexible GUI on the web page.”
“...it would be nice to resolve the latency issues.”
“...plan views of IPW data would be great to have overlaid onto model data in AWIPS.”
The forecaster at Key West asked for a station at Marathon Key.  Since FDoT has a site there, and we have been using it to produce IPW for some time, we need to keep the local forecast offices up-to-date on the resources available to them. 

- The responses were:
- “...more flexible GUI on the web page.”
- “...it would be nice to resolve the latency issues.”
- “...plan views of IPW data would be great to have overlaid onto model data in AWIPS.”
- The forecaster at Key West asked for a station at Marathon Key.  Since FDoT has a site there, and we have been using it to produce IPW for some time, we need to keep the local forecast offices up-to-date on the resources available to them.

**5)****	****When is the data the least useful?**

**Survey Results**

**6)****	****What can we do better?   What can we improve upon?**

## The responses were mixed.
“...we look at all these data sources.”
“...never look at satellite soundings.”
“...use time series plots of IPW off your website.”

All responses stated that the GPS data are extremely useful and they tend to look at the data more than the forecast discussions may indicate. 

- “...we look at all these data sources.”
- “...never look at satellite soundings.”
- “...use time series plots of IPW off your website.”
- All responses stated that the GPS data are extremely useful and they tend to look at the data more than the forecast discussions may indicate.

**7)****	****When looking at soundings, do you look at raob soundings or satellite soundings?**

**Survey Results**

**8)****	****Do you have any questions or additional comments?**

**Notes:**
Re: the last point in the first bullet.  This forecaster probably thinks that GPS-IPW is a sounding and it’s not.

## Survey Results

- Some of the other responses included:
- “this data has been very valuable to the forecast staff here”
- “put a quick user survey on your website to try and get more feedback about the data.”
- “I hope people realize the positive impact that this data has on forecast operations.  To me it’s critical.”

## NOAA Forecast Systems Laboratory

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing Systems    **
**2004 Technical Review**
**Transition to Operations**

**Kirk Holub                                                              ****GPS-Met Observing Systems Branch                                   **
**January 27, 2004**

## GPS-Met Project Objectives

- 	Demonstrate the major aspects of an operational GPS IPW monitoring system. [Satisfied since April 2000.]
- 	Facilitate assessments of the impact of these data on weather forecasts. [Continuous since 1998.]
- 	Encourage the use of GPS meteorology for atmospheric research and other applications. [Continuous since 1994.]
- 	Assist in the transition of these techniques to operational use.

**1**

**2**

**3**

**4**

## GPS-Met System Attributes

- FSL Prototype

- NWS Operational System

- Provides shelter from the elements
- Provides a place to store some 'stuff‘
- Cozy, single family dwelling
- Wooden frame construction
- Cedar shake roof
- Utilities?
- Adds security as required

- Provides shelter from the elements
- Provides a place to store a lot of 'stuff'
- Provides room for growth
- Built using reinforced concrete
- Has a tile roof
- Has a crawl space to access utilities
- Has built-in security features

**GPS-Met System Attributes**

## Operational System Upgrades

- GPS-Met prototype was designed to test ideas and flesh-out requirements, not to be an operational system:             -	reliability was not a driver;			                -	system has several points of failure and no 	automated fail-over capabilities.
- Prototype lacks flexibility:			                 -	implementation is fixed to accommodate a 30-min 	data acquisition/processing cycle;		                -	schedule driven instead of event driven;	              -	consists of many highly coupled, but loosely 	coherent scripts.
- Components are not well integrated:		                -	database is used primarily by the web site;	             -	adding or deleting sites, and changing the 	configuration of the sub-networks is a time 	consuming manual process.

## System Attributes

| Delivers IPW estimates every                       30 minutes | Deliver IPW estimates at arbitrary (user selectable) times |
| --- | --- |
| Data available in ASCII, netCDF, and BUFR formats | Data available in any required format |
| Data stored as ASCII flat files | Data stored in a relational database |
| Schedule driven | Event driven |
| Many separate programs that interact with each other | Fewer, well integrated programs |
| Database primarily supports web applications | Database is an integral component of the entire system |
| Can process hundreds of stations | Can process thousands of stations |
| Manual configuration change management and semi-automated site addition/deletion capabilities | Automated configuration change management including site addition/deletion capabilities |
| Partially documented | Fully documented |

**System Attributes**

- FSL Prototype System

- Operational System

## Critical Operational Needs

- Reliable GPS data collection from CORS:	                  -	reliability for “backbone sites” > 90%;	                -	reliability for “infill sites” ~ 80%;		                 -	reliable, low latency data delivery was never part of 	NGS/CORS requirements;			            -	staffed 24/7 operation is needed.

**Critical Operational Needs**

## Critical Operational Needs

- Reliable Response to System Problems:		              -	operator needed on duty 24/7;			            -	sys admin/analyst needed on call 24/7.

**Critical Operational Needs**

## Other Transition Issues

- FSL-owned GPS Receivers:			                 -	systems at NPN sites, some WFOs, and other 	locations are near end-of-life and will need to be 	replaced soon.
- GPS at other NOAA sites:			                -	systems at UA sites to validate raob moisture 	soundings;					             -	upgrade RRS?
- Location of the GPS-Met Operations:		             -	FSL uses network solutions and long-baseline 	fiducial sites to estimate ZTD;		            -	centralized data collection & processing is preferred 	but not necessary;					              -	centralized monitoring is also preferred but not 	necessary.

## Other Transition Issues

- Reducing Data Latency from “Infill Sites”:	                  -	infill sites are primarily owned by non-federal 	government agencies and universities;	            -	these organizations rarely need data in real-time;	              -	as a rule, they are willing and technically capable of 	providing low latency data to NOAA at no cost, but 	sometimes lack the resources to do so;	               -	a good example is the Plate Boundary Observatory.

## Other Transition Issues

- PBO is part of the EarthScope Program funded by NSF.
- It will consist of about 120 continuously operating reference stations with ~200 km spacing in the western U.S. and Alaska.
- Each station will have GPS and Sfc Met sensors, but data will only be retrieved once per day unless other provisions are made.
- Should NOAA provide resources to other agencies to upgrade and maintain PBO (or other agency) sites used for GPS-Met?
- And what about SuomiNet?			              -	not supported by NSF after 2005;		             -	should NOAA “adopt” these sites, and 	collaborate with/support university-owners?

## Transition Timelines

- In-House Development

- Outsourced Development

**Transition Timelines**

## Conclusion

- The prototype system does what was expected of it.
- Many critical issues must be addressed in order to achieve 7/24 operations; but all are achievable
- If the outsourced development approach is used, an operational system could be online in 3+ years
- If the in-house approach is used, an operational system could be commissioned by 4+ years

## NOAA Forecast Systems Laboratory

**NOAA Forecast Systems Laboratory**
**GPS-Met Observing Systems    **
**2004 Technical Review**
**Concluding Remarks**

**Seth Gutman                                                              ****GPS-Met Observing Systems Branch                                   **
**January 27, 2004**

## GPS Satellite Orbits

- The ability to compute sufficiently accurate GPS satellite orbits in real-time has been a fundamental technical driver in GPS meteorology.
- In the past, orbit quality control was not an issue because the GPS constellation was performing well, and there was lots of time for the IGS Orbit Centers to edit the data and compute the orbits.
- The introduction of the SOPAC hourly orbit in 2000, and the implementation of the sliding window processing technique, essentially solved the real-time problem... except for one thing.
- In the past year, there have been several satellite maneuvers that have adversely effected the quality of our retrievals, and negatively impacted the models assimilating them.

## GPS Satellite Orbits

- Improved techniques to identify orbit prediction errors are ***absolutely essential for operational use of GPS-Met***.
- Possible approaches include:			                      -	apply autonomous quality control procedures that 	allow an errant satellite to be...			-- 	identified, 						--	its data removed from the ZTD solution, and	-- 	its status monitored until the orbit can be 		accurately predicted again; or		               -	get real-time notification from USAF of a change 	in status of a satellite because of a problem or 	maneuver...					          	--	data from the satellite is not used until it is 		stabilized and returned to FOC.

## GPS Impact on NWP Forecasts

## Products and Services

- What success we have had in of our ability to:	            -	monitor GPS-Met accuracy;			                 -	evaluate data and test hypotheses;		                -	understand how mesoscale models like the RUC 	handle integrated observations like GPS;	               -	provide forecasters with timely moisture 	information;					                     -	and facilitate our outreach activities;                                                                        are in large part attributable to the products and services we have developed to serve various user communities.
- Engaging users directly, especially NWS, has resulted in:							             -	proposed incorporation of GPS-Met and new 	profiler data into the next build of AWIPS;	                   -	incorporation of GPS-Met into the next (and final) 	bundles of the operational RUC and Eta models;

## Products and Services

-       -	proposed addition of GPS-Met observation 	capability into the	next upgrade of the Radiosonde 	Replacement System (RSS);			           -	interest in using GPS-Met for validating NWS	rawinsonde moisture soundings;		           -	interest in using data from the IGS global network 	to build a global comparative data set for the next 	reanalysis of AMSU temperature and moisture 	measurements.					             -	the possibility of NOAA providing operational 	tropospheric and ionospheric signal delay models 	for HA-NDGPS to DOT and USCG.

## Applications

- Forecasters appear to be reasonably satisfied with our efforts to provide reliable products and services.
- Subjective use of GPS-Met is in its infancy.
- Expanded use at WFOs will depend on their ability to access these observations via AWIPS.
- Developing useful AWIPS applications in collaboration with other FSL Divisions and the WFOs is an exciting challenge that we are eager to take on.

## Any Questions?

- Given adequate orbit QC, there are no obvious technical reasons why GPS-Met cannot transition to NWS operations in the relatively near future.
- The cost and time to do so is modest compared to other system transitions.
- We’re ready to go!