# Presentation: 128096

## Climate applications of a global, 2-hourly atmospheric precipitable water dataset from IGS tropospheric products

**Junhong  (June) Wang**
***Earth Observing Laboratory***
***National Center for Atmospheric Research***

**Collaborators: Liangying (Liz) Zhang (EOL), Aiguo Dai (CGD), Teresa Van Hove and Ted Iwabuchi (UCAR/COSMIC), and Joel Van Baelen (CNRS)**

**Thank Support from NOAA Climate Change Data and Detection program**

**Notes:**
Today I am going to first present you results on “climate applications ..”, then make some recommendations on our future needs on IGS TP. 

## Outline

**The analysis technique and GPS PW dataset**
**Application #1: Quantifying systematic errors in global radiosonde humidity data**
** ****Application #2: Diurnal variations**
** ****Summary**
** ****Future needs **

**Notes:**
Here is the outline of my talk. I will first briefly summarize our ..., then show you two applications of this dataset, namely ..., and in the end make some recommendations on future needs.

## How does GPS estimate precipitable water? 

**ZWD = ZPD - ZHD**

**PW = ******** * ZWD**
****** ****= f (Tm)**

**ZPD = ZHD + ZWD**

**How does GPS estimate precipitable water?**** **

**Notes:**
The effect of mesosphere WV on the retrieval.
Next Q is how does .. And why we want to use the GPS measurements.
GPS satellites transmit radio signals to ground-based GPS receivers. The radio signals are delayed by the atmosphere, so you get this excess path length. The atmosphere delay comes from the TR and IN. By using two channel receivers, the IN delay can be removed. The TR delay is a combination of dry and wet delay. Total TR delay can be mapped to the zenith which is called zenith path delay. ZHD is a functions of Ps, and PW is proportional to ZWD by this II factor, which mainly depends on WV-weighted mean atmospheric T.
In a simple language, the noise for geodetic measurements becomes the signal of meteorology. 
The signal delays caused by the ionosphere normally exceed those of the lower atmosphere by a factor of  2-5. 
Geodesists have developed ways to treat tropospheric delays as a “nuisance parameter” and remove them to achieve sub-centimeter positioning accuracy.
In GPS Meteorology, we take these discarded signal delays and use them to monitor the refractivity of the atmosphere directly above a GPS site.

## A global, 11-year, 2-hourly PW dataset from ground-based GPS measurements(Wang et al. 2007, JGR)

**A global, 11-year, 2-hourly PW dataset from ground-based GPS measurements*****(Wang et al. 2007, JGR)***

**Notes:**
We have applied the AT to the IGS ZPD data from 1997 to dec. 2007 and created a .... The blue dots show the IGS stations. We also integrated the GPS data from the regional network, such as SumiNet (black) and GEONET (red) into the dataset. Currently we have ... We will continue to update this dataset and add more data from regional networks. 
5K error in Tm corresponds to 1.7-2% error in PW
An error of 2 hPa in Ps corresponds to ~1 mm error in PW

## Highlight of GPS-PW data

**Highlight of GPS-PW data**

**Notes:**
This is just one highlight of the data: top panel shows the time series of PW from Feb. 1997 to Dec. 2006 in Miami, then the low panel shows the blow-up of the data in JJA of 2006 from both GPS and radiosonde (red dots). The top panel shows regular seasonal variations and day-to-day variability within each month. The lower panel shows the day-to-day variability more clearly and also indicates the radiosonde values are systematically drier than GPS. You also see this record PW value of ~70 mm in August of 2006. So what is going on?

## Hurricane Ernesto 

**Hurricane Ernesto **
**(24 Aug – 1 Sep. 2006)**

**Notes:**
This shows PW (red) and Ps (blue) from 8/28 to 8/31 in 2006. This record PW is due to the pass of hurricane Ernesto. Here is the track of Ernesto. It approached Miami around 00UTC of Aug. 30 when the dramatic surface pressure decreasing was recorded too. 

## Problems:

_**Problems:**_
**Errors and biases**
**Spatial and temporal inhomogeneity**
**Spatial sampling errors**
**Diurnal sampling errors**

_**Results:**_** The role of radiosonde observations in climate studies is limited. **

_**Solutions**_**: To quantify radiosonde errors and correct them. **

**Notes:**
Here is a map of global radiosonde stations colored by radiosonde types. Globally there are 15 radiosonde types and about 900 stations. Immediately you see the spatial variations of colors. Keep it in mind that this map also changes with time for both the colors and possibly the locations.
In summary, global radiosonde data have these problems: each radiosonde and each sensor have its own errors and biases. The global radiosonde dataset has spatial .. 
Because of the problems, the role ... 
 Does this mean that we give up the radiosonde data? No. We need to try to solve the problems. The solution we offer here is to quantify ... There have been a lot of efforts on correcting global radiosonde temperature records, but much less for humidity. In this work, we will use the GPS estimated PW as a reference to quantify the radiosonde humidity errors.   

## Matched GPS and radiosonde data

**Matched GPS and radiosonde data**
**(< 50 km in distance, < 100 m in elevation, < 2 hours; 14 types and 136 stations)**

**Humidity sensors:**
**Capacitive**
**Carbon hygristor**
**Goldbeater’s skin**

- Wang and Zhang (2008a)

**Notes:**
Next I am going to show the application of using GPS PW data as the reference to quantify the radiosonde humidity errors. 
We match GPS and RAOB stations with horizontal distance < ... , and found 136 stations, where forty ... This map shows the station distribution colored based on radiosonde types. 電容性的 resistence耐性 
There are three types of humidity sensors used here, capacitive with cold colors and CH and GS with warm colors. As you can see, capacitive and CH are used in America, GH and CH used in Russian and Asia, and the rest of the world launches capacitive.   
Pls. note that currently there are 15 types of radiosondes used in global radiosonde network. Only Swiss radiosonde (ML-SRS) is not included here. But only one station in Switzland. 

## Systematic errors – mean biases

- Wang and Zhang (2008a)

**Notes:**
This plots show the median differences in PW between IGRA and GPS at 106 stations where the bias is statistically significant. S.D. of difference is also shown.  Different colors represent different radiosonde types. 
You see that there is a dry bias at most of stations using capacitive sensor, but moist bias for CH and GS. CH has much larger SD. This is consistent with what we know about these hygrometers. 
Beside mean bias, we also look into the characteristics of the bias. Here is just one example.

## Impacts of the sensor boom cover on Vaisala RS80 dry bias

- Wang and Zhang (2008a)

**Notes:**
This shows the Vaisala RS80 sensor module. Here is the T sensor, this is RH sensor. This silver cap is for humidity sensor to prevent rain, ice and solar radiation. In the end of 2000, the manufacture introduced this new sensor boom cover to prevent the contamination of the hygrometer that causes a dry bias in humidity measurements. Let’s look at the impact of ... 

## Temporal inhomogeneity of radiosonde PW data

**Temporal inhomogeneity of radiosonde PW data**

*Wang and Zhang (2008)*

**Miami, U.S.A**

**Suwon-Shi, Korea**

**Beijing, China**

**Relative PW differences (% Radiosonde-GPS)**

**Notes:**
One of the most serious problems with radiosonde data for climate studies is temporal inhomogeneity. And the TI is very hard to quantify without any reference and good metadata. 
This shows relative monthly mean PW differences between RAOB and GPS at three stations ...You can see the systematic errors in radiosonde data which can be as much as 20-30% and their changes with time. The known radiosonde type changes can partially explain this. “click”. Miami, June 1, 1998, 2001. ... You can see more details on the study of “systematic errors ..” in Wang and Zhang (J. Climate, in press). For the Korean station, we could not find any change associated with this PW difference change. We have used several sources for radiosonde metadata, but they are still not complete and sometimes not accurate. This demonstrates the usefulness of GPS PW data for identifying the discontinuities in radiosonde and potentially correcting them. 
J/Yang uses the carbon hygristor. Shang-E uses the CH sensor.

## Impacts of temporal inhomogeneity

- Carbon
- hygristor

- Capacitive

**Notes:**
The temporal inhomogeneity in radiosonde data can produce spurious climate trends. This is an example for Miami. The monthly mean PW anomalies are shown for radiosonde (blue lines) and GPS data (red lines). On last slide I have explained that this change around June 1998 from positive anomaly to negative ones is due to the change of humidity sensor from CH to CP. You can see that two datasets can predict opposite signs of PW trends. This would be misleading. The negative trend from RAOB is mainly due to this moist bias of CH. 

## PW diurnal variations in four regions

**Europe**

**30-70S**

**N.H. Mountains**

**Darwin region**

**Month**

**Month**

**Month**

**Month**

**LST**

**LST**

**LST**

**LST**

- Wang and Zhang (2008b)

**mm**

**Notes:**
Another application of the 2-hourly GPS PW dataset is to study WV diurnal variations. 
This shows seasonal variations of PW diurnal and sub-monthly variability  in four regions: Europe, 30-70S, .... for four seasons. The color is the diurnal variation, and the black contours are submonthly variability. I am not going to details. You see the seasonal and region-to-region variations of PW diurnal cycle in both magnitude and phase. 
The sub-monthly variability is much larger than the diurnal variation. That is not surprising because the sub-monthly variability is mainly a result of weather systems, whereas various competing factors can contribute to the diurnal variation.
--------------------------------------------------
The PW diurnal cycle in Europe is strongest in summer with a amplitude of ~0.6 mm and is slightly weaker in fall and winter, but is negligible in spring. the local time of the maximum value makes a transition from early evening (~2000-2200 LST) in summer to late afternoon (~1600-1800 LST) in autumn and then to before noon (1000-1200 LST) in winter. The PW diurnal cycle in other three regions has similar phase in four seasons but different amplitude. The 30-70S has small diurnal cycle, while Darwin region has very large diurnal cycle with a peak-to-peak amplitude of larger than 2 mm. In the mountain region PW peaks from late afternoon to early evening and has the largest diurnal cycle in summer. 
Annually, Darwin experiences three distinctive climate patterns: 1) a dry continental regime from May to September; 2) a wet, monsoonal season, which sometimes involves cyclones, from December to March; and 3) a transitional period between the other two. 

## Seasonal variations of diurnal and sub-monthly variability over Europe

**GPS**

**NCEP/NCAR**

**JRA**

**ERA-40**

- mm

- Wang and Zhang (2008c)

**Notes:**
This shows seasonal ... I have explained the PW diurnal cycle in Europe from GPS data. A reanalysis is a comprehensive global, multi-decadal dataset generated by a constant state-of-art numerical data assimilation techniques using various past observations. 
The PW diurnal cycle is very weak and poorly represented in amplitude, phase and seasonal variation in all three reanalysis products. 
All three reanalysis datasets perform well in predicting sub-monthly variability and its seasonal and diurnal changes.

## Summary

**Dataset: ****A global, 11-year, 2-hourly GPS-PW dataset is created from IGS tropospheric products for various scientific applications. **
**Climate applications:**** The dataset is used to quantify systematic errors in global radiosonde PW data, validate global reanalysis products and study diurnal variations.**
**3. More information:**
- Wang, J., and L. Zhang, 2008: Validation of Atmospheric Precipitable Water in Three Reanalysis Products using Ground-based GPS Measurements, extended abstract for *Third WCRP International Conference on Reanalysis*, Jan. 28 – Feb. 1, 2008, Tokyo, Japan.
- Wang, J., and L. Zhang, 2008: Climate applications of a global, 2-hourly atmospheric precipitable water dataset from IGS ground-based GPS measurements, J. of Geodesy, accepted.
- Wang, J., and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from comparisons with ground-based GPS measurements. *J. Climate*, in press.
- Wang, J., L. Zhang, A. Dai, T. Van Hove and J. Van Baelen, 2007: A near-global, 8-year, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements, *J. Geophys. Res.*, 112, D11107. doi;10.1029/2006JD007529. .
- Wang, J., L. Zhang, and A. Dai, Global estimates of water-vapor-weighted mean temperature of the atmosphere for GPS applications. *J. Geophys. Res.*, 110, D21101, doi:10.1029/2005JD006215, 2005.

**Notes:**
Here is the summary. We have created ... and will continue updating this dataset. You are welcome ... This dataset has various applications. I presented two applications. Here is a list of references for more info on this project.

## Future Needs

***Recommendations on improving future IGS products***

**To continuously produce the ZTD product and  maintain its long-term stability and high quality **
**To reduce diurnal biases in the ZTD product**
**To improve and increase sfc-met data**
**To co-locate with radiosonde stations**
**To increase the spatial and temporal coverage**

**Notes:**
So far hopefully I have convinced you on the value of the 2-hourly GPS-derived PW data for climate studies. But we can always do even better by working together. 
Based on our experience with the IGS ZTD product, we would like to make the following recommendations on improving future IGS products. 
The LT stability of the data is essential for climate studies. I will show you one example to illustrate this point.
I have shown you that the GPS PW data are very important for WV diurnal cycle studies, so we need to make efforts to reduce .. and in Ps and Tm used for PW derivation. This is especially true because we are dealing with a PW diurnal cycle of less than 5% that corresponds to ~0.24% change in ZTD.
Surface meteorology data, especially surface pressure, are required for calculating the dry delay and removing atmospheric pressure loading. Currently there are ~90 IGS stations that provide surface meteorology data. However, surface meteorology data are often very noisy and can not be used without careful examination and quality control. Besides improving the quality of surface meteorology data, more stations with surface pressure and temperature measurements are also needed, especially with the availability of 5-min ZTD data, since only surface meteorology data can provide 5-min surface pressure data to calculate ZHD. Furthermore, co-located surface meteorology data can be very useful for climate process studies. 
I have shown you how valuable the GPS PW dataset is for monitoring the quality of radiosonde humidity data. However, we found that the small displacements of GPS and radiosonde stations in space can make the comparisons complicated because of large variability of humidity in both space and time. Radiosonde data can be used to derive the mapping function for GPS data processing.
Incomplete temporal coverage in the IGS ZTD data makes it difficult to calculate monthly, seasonal, or even annual mean values. From 10-year of GPS PW data, we only found 13 stations with a complete 10-year record for PW trend calculations. I would appreciate for any info on why we have so much data dropout in ZTD data. In addition, it is important to add more IGS stations and integrate other regional GPS networks into the IGS network to increase the spatial coverage. 

## 1. To maintain long-term stability and high quality of the ZTD product

**1. ****To maintain long-term stability and high quality of the ZTD product**

**Notes:**
This shows monthly mean PW values from the IGRA and GPS-PW data at Fortaleza, Brazil. The radiosonde (WMO 82397) and GPS (FORT) stations are 22 km apart. Two datasets show very good agreement on the regular seasonal cycle and the year-to-year variations. Nevertheless, monthly mean radiosonde PW values are smaller than that from the GPS-PW dataset, which is consistent with the well-known dry bias of the radiosonde used here. More importantly the magnitude of the dry bias increases with time from less than 5 mm before 2000 to larger than 6 mm in 2005. I think that Jim Ray has a study on the impact of  conical radome on ZTD. They found that the addition of a radome produces a positive bias of ~1 mm in PW estimates, which is not enough to explain the difference between the radiosonde and GPS and its variation with time. Therefore, it is still not clear what causes the increase of the dry bias in the radiosonde data in 2000. Such difference has significant impact on PW trend estimate. This shows monthly mean PW anomalies from the GPS (magita) and radiosonde (blue) data. The PW trends are given in dashed lines. Two datasets produce opposite trends. 

## 4. To co-locate with radiosonde stations

**4. To co-locate with radiosonde stations**

**Provide long-term, high-quality climate records**
**Constrain/calibrate data from more spatially-comprehensive global observing systems**
**Measure large suite of co-related climate  variables**

## Slide 19

## 5. To increase the spatial and temporal coverage

**5. To increase the spatial and temporal coverage**

**13 stations with complete 10-year (1997-2006) GPS-PW record**

**Notes:**
Next let’s look at the comparisons of long-term variations. We found 13 stations which have complete 10-year GPS record and compare the monthly ... with RAs. This is the correlation coefficients of monthly mean PW anomalies between RAs and GPS at 13 stations. All have larger than 0.8 CC except MAC1 where CC is a little bit smaller. It suggests that all three RAs perform well in simulating the interannual PW variations. 

## 5. To increase the spatial and temporal coverage

##  Initiation
	10/2004: GCOS Implementation Plan: “Key Action 12: To establish a high quality reference network of about 30 precision radiosonde stations and other collocated observations” (2005-2009)
Motivation
Problems for climate in accuracy, long-term stability
Changes in measurement systems
Objectives
Provide long-term, high-quality climate records
Constrain/calibrate data from more spatially-comprehensive global observing systems (including satellites)
Measure large suite of co-related climate  variables

** ****Towards a GCOS Reference Upper Air Network (GRUAN)**

** ****Initiation**
**	****10/2004: GCOS Implementation Plan: “Key Action 12: To establish a high quality reference network of about 30 precision radiosonde stations and other collocated observations” (2005-2009)**
**Motivation**
**Problems for climate in accuracy, long-term stability**
**Changes in measurement systems**
**Objectives**
**Provide long-term, high-quality climate records**
**Constrain/calibrate data from more spatially-comprehensive global observing systems (including satellites)**
**Measure large suite of co-related climate  variables**

**Notes:**
The network was first advocated by a white paper written by Kevin T. and was called in the implementation plan in KA12: ...for implementation by 2009. 
The motivation for such network is that current climate data records have .. The most serious problem is associated with frequent changes ..., which are not well documented and well characterized.
There are three objectives for the network: ...

## 2. To reduce diurnal biases in the ZTD product

- PW/PW= ZTD/ZWD
- PW/PW=5%
- ZWD=113mm ZTD=2334 mm
**Requirement**: ZTD/ZTD=0.24%

**Notes:**
This is station Cordell in Oklahoma, here shows the diurnal cycle for all major variables. PW, Tm and Ps. The red lines are for SuomiNet data, the black lines are for EOL data. ZPD is the common input for GPS and SuomiNet PW dataset. ZPD diurnal cycle is blue line in the upper panel with Y-axis on the right side. EOL PW diurnal cycle and SuomiNet PW diurnal cycle have quite different phase. EOL PW has a smaller peak value at about 2 o’clock in the early morning, while SuomiNet PW diurnal cycle peaks at about 2 o’clock in the afternoon. Ps diurnal cycles from two datasets have phase difference less than 2 hours, with similar amplitudes. In the mean time, We see much larger Tm diurnal cycle from SuomiNet Data. This is easily understood because SuomiNet used Tm-Ts empirical relationship to calculate Tm, Tm carries the diurnal cycle of surface temperature. EOL Tm calculated by Tm definition using reanalysis data has much smaller diurnal cycle. And this larger SuomiNet Tm peak value contributes to SuomiNet PW peak value shows above, which changed SuomiNet PW phase dramatically. We can also see that EOL PW has similar diurnal cycle with ZPD.

## 3. To improve and increase sfc-met data

**(Foster et al. 2000, 2003; Champollion et al. 2004)**

**3. To improve and increase sfc-met data**

**Notes:**
Time series of PW and rain rate on Nov. 1-2, 2000 for the Ka’U (Ka-oo) storm: precursory drying, rapid rise in PW, and PW peaks (>median+3SD). The question is that does this relation hold in other regions? If so, how does it vary with different regions? 

## 3. To improve and increase sfc-met data

**Change of P sensor on July 20, 2000**

**10/1999 ~ 7/2000**

**Notes:**
The surface meteorology data, especially surface pressure, are very useful for calculating the dry delay and removing atmospheric pressure loading. 
Currently there are ~70 stations that provide surface meteorology data. But we found that surface met data are very noisy and can not be used without careful examination and quality control. Just show you one example here. 
This shows the surface pressure from 12/99 to 3/02 from GPS-sfc met (red) and sfc synoptic data (black). Obviously something is wrong with the sfc met data from 10/1999 to 7/2000. Based on the data log files at Brussels, sfc P sensor was not working properly in this period, so the P sensor was changed on July 20, 2000. 

Beside the quality of sfc met data, we also need more stations with sfc met sensors, especially with the availability of 5-min ZTD data, only sfc met data can provide you 5-min Ps data to calculate ZHD. In addition, co-located sfc met data can be very useful for climate process studies. 

## Global PW diurnal anomaly

-  **The diurnal cycle is less than 5% of annual mean PW**
** ****Larger magnitude in summer than in winter**
** ****Peak around late afternoon to early evening **
** ****An order of magnitude smaller than seasonal variation**

**Notes:**
This is annual mean PW diurnal cycle for globe, SH and NH. We found that ...

## Diurnal sampling errors 

**~2/3 of global stations launch sondes twice daily, ~1/6 at 00 UTC and ~1/6 at 12 UTC**
**The sampling error is generally small (********2%) for 2-daily soundings**
**The sampling error can be as much as 10-15% for 1-daily sounding at some stations**
**Suggesting that in order to get a unbiased monthly mean PW, at least twice daily soundings are required.**

**Notes:**
Lastly let’s look at the ... we sub-sample the 2-hourly GPS PW data at 00 and 12Z to estimate the diurnal sampling errors. Because of cost, some countries are reducing, sometimes stopping, their soundings, this suggests that in order to get a unbiased monthly mean PW, at least twice daily soundings are required.
Here I use two examples to illustrate the point. Next, let’s calculate seasonal mean PW using only 00Z, only 12Z, 00/12Z and all data points. ELAT: for 00Z, overestimate PW by ~10%; underestimate by ~6.5% for 12Z; the average of 00/12Z gives you a better estimate.  However, for the Cyprus station, 00Z data underestimate PW by 5%, 12Z gives you the best estimate. 
The diurnal sampling errors depend on the magnitude and phase of the PW diurnal cycle. 

 Figure 12 shows the annual mean sampling errors at 220 stations for twice and once daily radiosonde data. Red and upward arrows are positive value, and the green and downward arrows are negative values. 
The annual mean sampling errors are generally small (within ±2%) for the twice-daily data, but can be as high as 10-15% and have magnitudes larger than 2% at 33% of the stations for the once-daily data (Fig. 12). The sampling errors for only the 01 UTC data are negative at most of the global stations (75% of the stations) except in South America where the large positive biases are shown (Fig. 12). The errors of only the 13 UTC data are generally opposite of those for only the 01-UTC data with positive biases at 69% of the stations. 

## Precipitable water (PW, TCWV, IWV, PWV)

**Precipitable water (PW, TCWV, IWV, PWV)**

**Notes:**
PW is the column-integrated WV from surface to the TOA and has a unit of mm. Sometimes people call it TCWV, IWV or PWV. In this talk I will call it PW. Global mean PW is ~25 mm. 
This shows annual mean PW for 1992. You see high values in Tropics and decrease polar ward, dryness over Mountains and desert. Highest PW values are over the warm pool region in TWP. High WV over ocean than over land
>50% of PW is below 850 mb, and >90% is below 500 mb. 

## Goal #4:Values for GRUAN – NAME (Summer 2004)

**Goal #4:Values for GRUAN ****– NAME (Summer 2004)**