Christian Sutton. Microwave Water Radiometer measurements of tropospheric moisture. ATOC 5235 Remote Sensing Spring 2003

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1 Christian Sutton Microwave Water Radiometer measurements of tropospheric moisture ATOC 5235 Remote Sensing Spring 23

2 ABSTRACT The Microwave Water Radiometer (MWR) is a two channel microwave receiver used to determine column integrated tropospheric liquid water and water vapor. The specific instrument analyzed in this paper is the Radiometrics WVR-11 radiometer. Retrieval techniques, underlying physics of the sensor, and comparisons to traditional sonde techniques will also be discussed. Introduction: The three phases of water have shaped the earth and permitted life to flourish. The two parameters of interest in this paper are columnar liquid water and columnar water vapor. These two parameters are of great importance in the scheme of atmospheric science. Moisture characteristics of the troposphere are important when initializing a modeling system. Incorrect characterizations and parameterizations of water can lead to model errors, and degrade the skill of a forecast. Moisture characterization of the troposphere used to be performed by balloons, or sondes. However, due to a host of problems the measurements were plagued by error and expensive to perform. Using a Microwave Water Radiometer (from this point referred to as an MWR) and some other meteorological instruments, moisture measurements can be made with much better accuracy than was previously available. MWR T b (2GHz) T b (31GHz) Radio Acousic Sounding System T v (z) Ceilometer z c Sfc Instruments T sfc,rh sfc,p sfc T(z) ρ V (z c ) ρ V () Retrieval of V,L (columnar quantities, vapor, liquid, (cm)) Retrieval of ρ V (z) ρ L (z) Initial ρ V (z),ρ L (z) Figure 1: Schematic adopted from Han & Westwater 95. Value added procedure for determining columnar water quantities and height profiles. T b is brightness temperature, z c is cloud base height, T v is virtual temperature. This process is used in the MWRPROF VAP. N iterations of physical retrieval and statistical analysis yields: ρ V (z),ρ L (z) V,L (cm) 2

3 Sensors and Retrieval Perhaps the most important process used to treat this data is the MWRPROF VAP (MWR Profile Value Added Procedure). This retrieval process uses the MWR, a laser cloud ceilometer, a radio acoustic sounding system (RASS) and surface meteorological instruments. The MWR measures the initial vapor and liquid column quantities (V and L, [cm]). An MWR is a useful tool when trying to determine the water distribution with height, but it is limited. By using other instruments in concert with the MWR, a much more accurate determination can be made. Radio Acoustic Sounding System The RASS is a wind profiler, but in this case used to determine the virtual temperature variation with height. Virtual temperature is defined as the temperature at a constant pressure a parcel of dry air must be heated to in order to have the same density as a parcel of moist air. This sensor is a radar system, so it is an active sensor. It transmits radio frequency radiation and determines the wind profile from backscattered radiation. The backscattering is due to changes in refractive index caused by turbulence. Low altitude profiles often have high resolution, while high altitude profiles can be measured, but suffer from poor resolution (Han 95) Belfort Laser Ceilometer The laser ceilometer is another active sensor used in conjunction with the MWR to determine the height of cloud bases. It can measure cloud bases up to 3.6km, with a temporal resolution of 3 seconds and a vertical resolution of 15 meters. Surface Meteorological Observation System The SMOS is a suite of in-situ instruments to measure standard surface meteorological parameters. Such parameters include barometric pressure, temperature, wind speed, wind direction, relative humidity, precipitation, and snow depth. Figures 2-4 RASS, Ceilometer, SMOS. Courtesy of ARM/DOE. 3

4 Microwave Water Radiometer The MWR is a two channel, sensitive microwave receiver. It measures a variety of other parameters other than columnar vapor and liquid water, such as microwave brightness temperature and infrared brightness temperature. Operationally, it also has quality control flags that can stop operation of precipitation is present, so as not to collect faulty data. The two channels the MWR operates at are 23.8GHz and 31.4GHz. Atmospheric water vapor emission is determined using the 23.8GHz channel, while the 31.4GHz channel is used to detect liquid water emission. Since there are two channels, measurements can be separated, and each water parameter can be determined. The MWR performs its measurements in line-of-sight (LOS) mode with a zenith angle of o. Zenith angle can be varied (tipping mode), but this data is difficult to use and only used for the purposes of calibration. Calibration curves from this tipped mode are not surprisingly referred to as tipping curves. Retrieval algorithm for the Microwave Water Radiometer The physics of the retrieval algorithm was discussed in ATOC 5235 Lecture 14 (Principles of Passive Remote Sensing/Measurements of Path Integrated quantities). In the case of the fundamental radiative transfer behind the MWR, scattering is ignored. The reason for this is as the size parameter for the scatterers gets small, the extinction parameter Q s gets very small, thus allowing us to ignore scattering effects. Since the measurements are made from the surface, the radiances on interest are, obviously, the down welling intensities. The following equation describes the measured intensity at the surface: (1) I = Ic exp( τ *) + ( B( T ( z)) ρ k exp( τ ( z)) dz The first term in (1) is the cosmic radiance that has survived extinction to the surface. The second term is the contribution from radiation emitted along the path towards the surface. Note this is the same equation as equation 14.9 from the lecture notes, when the variable of integration is changed from height to optical depth by absorbing the density and absorption coefficient. Since the measurements of interest are in the microwave region of the electromagnetic spectrum, the Rayleigh-Jeans approximation can be made the Plank function (lecture equation 4.4a). Equation (2) is the adjusted Plank function: 2 kb T c (2) B( T ) = 4 λ By combining (1) and (2) and inverting for brightness temperatures, equation (3) can be written in terms of a cosmic brightness temperature, T c, (2.75 K) and the atmospheric contribution. 4

5 (3) TB = Tc exp( τ *) + ( T( z) ρ k exp( τ ( z)) dz Equation (3) can be generalized in terms of N layers of the atmosphere, each contributing to the downwelling radiance. A simplification can be made by solving for an isothermal atmosphere. By making the assuming all layers have the same temperature, the brightness temperature can be solved in terms of a cosmic background brightness temperature and a frequency dependant atmospheric mean radiating temperature, T MR. (4) T Tc exp( τ *) + T (1 exp( τ *)) B = MR Equation (4) can be inverted in terms of optical depth: TMR TC (5) τ * = ln[ ] TMR TB Once the optical depth is known, the columnar amounts of liquid and water vapor can be determined with some external processing and climatological data. The optical depth is assumed to be composed of contributions from three terms: (6) τ* = τ dry + kv zv + k L zl The first term is the optical depth contribution due to emission from O 2 molecules. The other terms are the contributions due to water vapor and liquid water. The absorption coefficients are determined from the local climatology. Since the MWR measures radiation at two specific frequencies, there are two values for the total optical depth. This implies to linear equations that can be solved the columnar amounts of water. The columnar amounts can be expressed in terms of optical depths and some constants. The constants are calculated from a regression process run on the data. A similar treatment can be found on page of Stephen s text. The retrieval algorithm is used in context of the flow chart from Han, et al. After incorporating output from the other instruments now the column quantities can be estimated. The vertical resolution of the integrated measurements is 25 meters, and the readings are taken up to 12 km. The purpose of using the other instruments and the MWR is to improve accuracy. The purpose of the statistical analysis is to take into account situations that may lead to outlier measurements, such as precipitation events. Including statistically derived quantities (such as absorption coefficients, etc) greatly improve the accuracy of the MWR measurements. Data and Data Analysis Much of the data for this analysis came from the MWRPROF value added process. The advantage of using this data was that it included the raw MWR data in addition to the processed data with added information from the instrument suite. As stated earlier, the main parameters of interest were the column integrated amounts of liquid water and water vapor. One problem with microwave measurements is the lack of independent data to verify measurements. For this project, the MWRPROF measurements were compared 5

6 against sonde readings and some radar images for dates of interest. In some experiments, radiative transfer simulations were used as a data source to compare against the sonde and MWR measurements. In these cases, the MWR improved cloudy day vapor density profiles greatly. On sunny days, there was no improvement. Source data was taken from July 1 st, 22 until July 16 th, 22 and the Lamont, OK facility. Error Measurements The two primary errors were the retrieval error and the sonde error. The retrieval error was the error of direct physical inversion (for column vapor) versus the MWRPROF value. The sonde error was the error versus the same MWRPROF value. The inversion error (retrieval) was more positive during the day, but more negative during the evening. There were two peaks that occurred during the measurement period. These peaks correlated semi-strongly (ρ approximately.7) to the root mean square error in temperature. Since the MWRPROF procedure uses data from the profiler, it is possible that error propagated from the temperature measurement, and amplified error in the vapor column retrieval. A correlation was also performed between the sonde error and the rms temperature error, but it was a weak value of.4. On average, the physical inversion tended to overestimate the value of column vapor amount, while the sonde tended to underestimate the same value. In addition to this, several of the column vapor measurements were also compared to millimeter wave cloud radar measurements. The particular dates of interest were July 3 rd, 22 and July 11 th, 22. 6

7 7

8 Figure 5 and 6 Millimeter wave cloud radar soundings for July 3 rd and 11 th, 22. On July 3 rd, the MWR and sonde measurements detected high column water vapor. This matched up nicely to the radar measurements for that day. The high integrated column vapor measurement can probably be attributed to a mid-level cloud system overhead. On July 11 th, a strong sounding was made by the radar. This also matched up well with the high integrated column readings taken that day by the two instruments. On days were there were low values detected by the MWR, the radar soundings showed very little reflectance overhead (July 5, ~5pm GMT and July 13, ~11am GMT). There are two possible situations that might cause the liquid water amount to exceed ~3mm, and these are dew or rain. There were two strong signatures of high liquid water content. The column liquid water measurements matched closely to precipitation events on July 2 nd, 6 th -7 th, and 12 th. Relevance to modeling Unfortunately, in the course of numerical weather prediction and modeling, many physical processes must be parameterized. Errors in the model, or incorrect parameterizations can degrade the skill of a forecast or model output. A better understanding and measurement of vapor and liquid profiles can help determine more about clouds, but also assist in determining the profile of the atmosphere. A better 8

9 understanding can lead to better parameterizations of water-dependant processes, and help to act as a reference point for model output. Conclusions Traditional sonde techniques are still useful and the data collected from them has been useful for building statistical population sets for determining various atmospheric parameters. However, they are expensive, and they are drift in any winds aloft. The MWR is a useful tool for determining moisture characteristics of the atmosphere. It can be operated remotely, and takes a single column measurement. While it is a powerful tool, it is even more useful when used in conjunction with other instruments, such as surface meteorology instruments, ceilometers, and sounders. When compared to traditional techniques of just using MWR data, the suite of tools often provides much better results under cloudy conditions. 9

10 Additional Figures: Error in vapor column retrieval (retrieval - MWRPROF)/MWRPROF Date (GMT) 7/1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/ error.4 error VAP Error in Sonde measurement (Sonde - MWRPROF)/MWRPROF Date (GMT) 7/1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/ error error sonde VAP Figure 7, 8-Error from direct physical inversion & sonde measurements relative to MWRPROF data. 1

11 Density root mean square error as a function of time density (grams/cubic meter) density RMSE.4.2 7/1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/22 time & date (GMT) Temperature root mean square error as a function of time 25 2 Temperature (K) 15 1 temp RMSE 5 7/1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/22 time & date (GMT) Figure 9,1-RMS error in density & pressure from MWRPROF dataset. 11

12 Column liquid measurements vs. time columnar liquid amount (cm) MWR LIQ RET LIQ.1.5 7/1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/22 date & time (GMT) Precipitation as a function of time and date 12 1 mm of precipitation Precip (mm) 2 6/29/2 7/1/2 7/3/2 7/5/2 7/7/2 7/9/2 7/11/2 7/13/2 7/15/2 7/17/2 7/19/2 time and date (GMT) Figure 11, 12-Column liquid measurements versus time and precipitation versus time. 12

13 Column vapor measurements vs. time Columnar vapor amount (cm) MWR VAP RET VAP sonde vap /1/22 7/3/22 7/5/22 7/7/22 7/9/22 7/11/22 7/13/22 7/15/22 7/17/22 time and date (GMT) Figure 13 Column vapor measurements versus time. Note data is from MWR (with quality control checks), physical inversion, and sonde measurements. 13

14 References: Han, Y. and Westwater, E. R Remote Sensing of Tropospheric Water Vapor and Cloud Liquid Water by Integrated Ground-Based Systems. JAOT 12, Liljegren, J.C Two-channel microwave radiometer for observations of total column precipitable water vapor and cloud liquid water path. Fifth Symposium on Global Change Studies, pp January 23-28, 1994, American Meteorological Society, Nashville, Tennessee. Liljegren, J.C., and B.M. Lesht Measurements of integrated water vapor and cloud liquid water from microwave radiometers at the DOE ARM Cloud and Radiation Testbed in the U.S. Southern Great Plains. Presented at the IEEE International Geosciences and Remote Sensing Symposium, Lincoln, Nebraska. Solheim, F. Microwave Radiometer for Passively and Remotely Measuring Atmospheric Temperature, Water Vapor, and Cloud Liquid Profiles. Radiometrics Corp. Summary Document. ARM Microwave Water Radiometer summary pagehttp:// Water vapor retrievals from microwave radiometer data, combined with other ground based remote sensors - QME comparing the retrieved water vapor and temperature profiles from the VAP mwrprof with radiosonde profiles 14

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