Retrieval of tropospheric and middle atmospheric water vapour profiles from ground based microwave radiometry

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Retrieval of tropospheric and middle atmospheric water vapour profiles from ground based microwave radiometry René Bleisch Institute of Applied Physics 26.

1 Retrieval of tropospheric and middle atmospheric water vapour profiles from ground based microwave radiometry René Bleisch Institute of Applied Physics / 45 Outline 1 Introduction Measuring water vapour Outline 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 2 / 45

2 Outline Introduction 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 3 / 45 Introduction Vertical distribution of water vapour 2 Temperature [K] H 2 O vmr standard deviation Mesosphere Pressure [hpa] 1 Stratopause Stratosphere 4 3 Altitude [km] Pressure [hpa] Altitude [km] 2 Tropopause Troposphere 3 1 H O vmr [ppm] Standard dev. [%] average and standard deviation from ECMWF-data over Bern (27-211) two different regimes : troposphere and middle atmosphere (stratosphere+mesosphere) 4 / 45

3 Troposphere Introduction contains most of the water vapour strong vertical gradient high spatial and temporal variability 5 / 45 Troposphere Introduction Weather Climate condensation to clouds precipitation contributes 6% of the natural greenhouse effect saturation vapour pressure increases with temperature (+7% per Kelvin) strong positive feedback effect between temperature and water vapour content e s (T) [hpa] Saturation vapour pressure over liquid water T [ o C] 6 / 45

4 Introduction Middle atmosphere water vapour is a trace gas low spatial and temporal variability formed by methane oxidation (and transport through tropical tropopause) long lifetime tracer for dynamical processes source of OH-radicals linked with O 3 -chemistry 7 / 45 Introduction Measuring water vapour Measuring water vapour observation of water vapour is important both in troposphere and middle atmosphere In situ measurements direct measurement of water vapour by weather stations, balloon soundings, aircrafts or rockets Remote sensing Indirect measurements, inversion of atmospheric properties from radiation measurements passive (emission, absorption) or active (backscattering) observations groundbased, airborne, satellite 8 / 45

5 Introduction Measuring water vapour Measuring water vapour most techniques cover either troposphere or middle atmosphere due to the strong contrast between troposphere and stratosphere, combined measurements are very difficult UTLS-range is most difficult to measure (only covered by few instruments) 9 / 45 Outline Microwave radiometry 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions / 45

6 Microwave radiometry Microwave range from Janssen / 45 Microwave radiometry Radiative transfer Basics of radiative transfer in MW-range Planck s Law B ν (T ) = 2hν3 c 2 hν << kt in MW-range (Rayleigh-Jeans Limit): 1 e hν/kt 1 B ν (T ) 2ν2 k T intensity temperature c2 Rayleigh-Jeans Brightness Temperature Tb(ν) = c2 2ν 2 k I ν Temperature of a blackbody emitting radiation with Intensity I ν Radiative transfer equation Tb(ν) = Tb e τ(z ) + z T (z)e τ ν(z) k a dz 12 / 45

7 MW-spectra Microwave radiometry Radiative transfer Brightness temperature [K] Simulated spectra for (IWV=19.2mm) 3 O 2 2 H O clouds all Range of Frequency [GHz] Calculated with Arts using ECMWF-data 13 / 45 The 22 GHz-H 2 O-line Microwave radiometry The 22 GHz-H 2 O-line excited by rotational transitions of H 2 O-molecule line is pressure broadened linewidth is altitude dependent lineshape vertical distribution Altitude [km] Linewidth of the GHz H 2 O line Pressure Broadening Doppler Broadening Linewidth Linewidth [Hz] V W Line Shape [MHz 1 ] Lin / 45

8 Retrieval Microwave radiometry Forward Model y = F (x, b) + ε Inversion ˆx = F 1 (y, b, ε) y: measured spectrum, x: true profile, ˆx: retrieved profile, b: forward model parameters, ε: measurement noise Problem: inversion is not unique ill-posed problem 15 / 45 Microwave radiometry Optimal estimation technique after Rodgers How to restrict solutions? ˆx = x a + D y (y F (x a, b)) where D y = ( K T x S e 1 K x + S a 1 ) 1 K T x S e 1 use of a-priori knowledge consider the uncertainties in measurement and a-priori OEM searches a solution which minimizes cost-function from Bayes theorem: χ 2 = [y F (ˆx, b)] T S 1 e [y F (ˆx, b)] + [ˆx x a ] T S 1 a [ˆx x a ] }{{}}{{} spectrum cost profile cost x a D y S e S a K x a-priori profile (eg. balloon soundings or satellite data) contribution function matrix ( ˆx/ y) measurement covariance a-priori covariance weighting function matrix ( F / x) 16 / 45

Nonlinear OEM Microwave radiometry ˆx = x a + D y (y F (x a, b)) where D y = ( K T x S e 1 K x + S a 1 ) 1 K T x S e 1 The above equation is only valid if F (x) is linear Nonlinear case requires

9 Nonlinear OEM Microwave radiometry ˆx = x a + D y (y F (x a, b)) where D y = ( K T x S e 1 K x + S a 1 ) 1 K T x S e 1 The above equation is only valid if F (x) is linear Nonlinear case requires iterative approach (Marquardt-Levenberg is used) x i+1 = x i + ( ) K T i S 1 e K i + S 1 a + γd 1 1 [ a K T i S 1 e (y F (ˆx, b)) S 1 a (x i x a ) ] iteration is initialized with x i = x a iteration is stopped, if convergency is reached K i weighting function matrix evaluated at x i ( F (x i )/ x i ) D a diagonal matrix of S a γ trade-off parameter 17 / 45 Averaging kernel Microwave radiometry Averaging kernel matrix A characterises the vertical smoothing properties of the retrieval A = D y K x = ˆx/ x the smoothing can be applied on a reference profile: ˆx ref = x a + A (x ref x a ) Averaging kernels rows of A, response of retrieval to a perturbation at a specific altitude in the true profile 18 / 45

10 Microwave radiometry Averaging kernel Measurement response (sensitivity) sum of the columns of A, sensitivity of the retrieval Full-width at half maximum width of the AVKs, indicator of vertical resolution 19 / 45 Outline 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 2 / 45

Instrument 22 GHz MW-radiometers There are only few 22 GHz-H2 O radiometers

roof of ExWi-building, since 27 at Zimmerwald observatory SWARA sister

instrument, built 28, now in Sodankyla (Finland) 21 / 45 photos courtesy N.

11 Instrument 22 GHz MW-radiometers There are only few 22 GHz-H2 O radiometers equipped with spectrometers, IAP operates three: built 22, located first on the roof of ExWi-building, since 27 at Zimmerwald observatory SWARA sister instrument of, built 26, located in Seoul (South Korea) -C compact campaign instrument, built 28, now in Sodankyla (Finland) 21 / 45 photos courtesy N. Ka mpfer Instrument (MIddle Atmosphere WAter vapour RAdiometer) from AIUB-homepage courtesy M. Canavero designed to measure water vapour emission line at 22 GHz and retrieve middle atmospheric water vapour profiles continous operation, except rain 22 / 45

12 Tb = Tb line Tb ref (1) Instrument Instrument Front-end: Backend: horn antenna rotatable mirror calibration loads digital Acqiris-FFT-spectrometer with 16 channels, BW 1 GHz, Res. 61 khz 23 / 45 Calibration Balancing calibration Dicke switching switching between line (sky@15-2 ) and reference observation (sky@85-9 +reference absorber) difference spectra 24 / 45

Balancing calibration Calibration Correction for troposphere and observation angles spectrum as measured at tropopause under θ= y = Tb a correction factor a derived from opacity, observation angles

13 Balancing calibration Calibration Correction for troposphere and observation angles spectrum as measured at tropopause under θ= y = Tb a correction factor a derived from opacity, observation angles and attenuation of reference absorber Tb [K] Balanced spectrum observed fitted Frequency [GHz] 25 / 45 Tipping calibration Calibration Tipping curves performed each 2 minutes, observations of the sky under several angles (between 2 and 6 ) and an absorber at ambient temperature ( hot load ) are used to derive opacity τ tropospheric correction for balancing 26 / 45

14 Calibration Tipping calibration Total power spectrum with absolute Tb Tbsky (θ, ν) = T e τ (ν) µtr (θ) + Ttrop (1 e τ (ν) µtr (θ) ) Total power spectrum Tb [K] observed fitted Frequency [GHz] / 45 Middle atmosphere retrieval Middle atmosphere retrieval H2O (48h retrieval) / Meas. resp. >.6 (white line) H2O VMR [ppm] Pressure [hpa] Jul27 Jan28 Jul28 Jan29 Jul29 Jan2 Jul2 Jan211 Jul211 Jan212 Jul212 courtesy D. Scheiben balanced spectra (center part, full resolution) a-priori: Aura/MLS vertical range: 35-8 km linear OEM vertical resolution: km temporal resolution: hours-days 28 / 45

15 Tropospheric retrieval Tropospheric retrieval total power spectra (full bandwidth, 2 Mhz resolution) 211 Pressure (hpa) H O VMR [/ ] (4h Trop. retrieval) / Meas. resp. >.6 (white line) 212 vertical range: 2-7 km nonlinear OEM a-priori: campaign balloon soundings from Thun vertical resolution: 3-5 km temporal resolution: typically 2 hours 29 / 45 Tropospheric retrieval Comparison Pressure (hpa) H2O VMR [ /] (2h Trop. retrieval) / Meas. resp. >.6 (white line) 26. correlation of up to.9 Pressure (hpa) wet bias of -2% H2O VMR [ /] SND Payerne 26.9 corresponds well with operational soundings from Payerne / 45

Improving the tropospheric retrieval Outline 1 Introduction Measuring water vapour 2 Microwave radiometry

retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6

Meteo station delivers relative humidity, pressure and temperature vmr at surface, operationally used for

16 Improving the tropospheric retrieval Outline 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 31 / 45 Improving the tropospheric retrieval Use informations from other available instruments Meteo station delivers relative humidity, pressure and temperature vmr at surface, operationally used for a-priori Ceilometer cloud base altitude IR-sensor attached to cloud base temperature RH@cloud base % vmr value at cloud base 32 / 45

17 Constraining in OEM Improving the tropospheric retrieval Altitude [km] Range of values range x a vmr@cloud base H O vmr [ / ] 2 only soft constraining to a range of values ( x a ±std) 33 / 45 Fixed-Point approach Improving the tropospheric retrieval Altitude [km] Range of values range x a vmr@cloud base "Fixed Point" H 2 O vmr [ / ] hard constraining to a known value ( fixed-point ) set std@fp to a low value (not ) 34 / 45

18 Improving the tropospheric retrieval Example standard retrieval (1) and retrieval with fixed-point at cloud base (2), compared with operational sounding data from Payerne 35 / 45 Difficulties Improving the tropospheric retrieval delta-peak like features in S a ( adaptations of retrieval grid) cloud base temperature not known with enough precision vmr = RH e sat(t) p +1%RH +1% in vmr but +1 K +5-% in vmr maximum of RH and maximum of vmr not at the same altitude comparison snd is affected by spatial distance 36 / 45

19 Outline Combined retrieval of troposphere and middle atmosphere 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 37 / 45 Combined retrieval of troposphere and middle atmosphere Balanced spectra without trop. correction (after G. Nedoluha, WVMS-retrieval) Differences to classical retrieval: entire bandwidth (1 GHz) is used no correction for troposphere and zenith angles ( y = Tb) In principle allows to retrieve troposphere and middle atmosphere from balanced spectra Implications: forward model needs to be calculated separately for line and reference observation: same applies for weighting functions: F (x) = F line [F ref A ref + T ref (1 A ref )] K x = K x (θ line ) K x (θ ref ) A ref 38 / 45

Combined retrieval of troposphere and middle atmosphere Nedoluha approach: Results Simulation 211 9 27 12: (tint=6h) 9 shows promising results 8 with sensitivity both 8 in middle atmosphere and

20 Combined retrieval of troposphere and middle atmosphere Nedoluha approach: Results Simulation : (tint=6h) 9 shows promising results 8 with sensitivity both 8 in middle atmosphere and troposphere xa x snd snd conv. MIA clas df: 7. H: Application to real observations Altitude [km] 6 not possible due to a baseline induced by the reference load (period 3 MHz) used in classical retrieval BT [K] H 2 O mixing ratio [ppm] H 2 O mixing ratio [ppm] y Combined retrieval of troposphere and middle atmosphere yf Frequency [GHz] Retrieval of full resolution total power spectra Res. [K] AVK [%/%].4 sigma:. K.2 simulated averaging 39 / 45 kernels Frequency [GHz] Classical TP Tropo TP (σ=) TP (σ=.2) TP (σ=.4) like troposphere retrieval, but using total 8 8 power spectra with full spectral resolution (61 khz) to be 7also sensitive 7 in middle atmosphere Simulation Altitude [km] indicate sensitivity in both middle atmosphere and troposphere, 3 but 3 sensitivity in middle2atmosphere strongly depends on measurement noise.1.2 AVK [ppm/ppm] AVK [%/%] AVK [%/%] AVK [%/%] AVK [%/%] simulated averaging kernels 4 / 45

21 Combined retrieval of troposphere and middle atmosphere Retrieval of full resolution total power spectra Radiometer noise formula: Tb = f T sys νtint Noise sensitivity against opacity (t avg =6 hours).12.1 T sys ν t int f System noise temperature: T sys = T receiver + T obs.6 frequency resolution integration time.4 factor for receiver type: total power: f = 1 classical: f = a 2.2 Nedoluha: f = Tb [K].8 opacity (τ) [ ] classical Nedoluha Tp low res Tp full res real observations are affected with a high noise level, up to 1/3 of the difference between line peak and background (.27 K) prevents any useful application 41 / 45 Combined retrieval of troposphere and middle atmosphere Combination of classical and tropospheric retrieval combination of tropospheric and classical approach to a single approach may be possible, but requires significant adaptations of the retrieval software leads to problems with convergency (general problem of all approaches combining middle atmosphere and troposphere) issues with polynomial fitting 42 / 45

22 Conclusions Outline 1 Introduction Measuring water vapour 2 Microwave radiometry Radiative transfer The 22 GHz-H 2 O-line 3 Instrument Calibration Middle atmosphere retrieval Tropospheric retrieval 4 Improving the tropospheric retrieval 5 Combined retrieval of troposphere and middle atmosphere 6 Conclusions 43 / 45 Conclusions Conclusions instrument is considered for middle atmosphere, but also delivers information about troposphere however, for tropospheric application it is inferior to conventional 22 GHz radiometers but, the instrument provides measurements for -8 km with gap in UTLS performance may be improved by taking into account additional information e.g. from Ceilometer combined retrieval of troposphere and middle atmosphere is theoretically possible (Nedoluha-approach), but its application to is limited by baseline issues generally, combined retrievals tend to have problems with convergency 44 / 45

23 Thanks to all for your support during the last years!

Retrieval of tropospheric water vapour by using spectra from a microwave spectro-radiometer at 22 GHz

Retrieval of tropospheric water vapour by using spectra from a microwave spectro-radiometer at 22 GHz René Bleisch, Niklaus Kämpfer, Dominik Scheiben Institute of Applied Physics, University of Bern ISTP,