GPS RO Retrieval Improvements in Ice Clouds

Transcription

1 Joint COSMIC Tenth Data Users Workshop and IROWG-6 Meeting GPS RO Retrieval Improvements in Ice Clouds Xiaolei Zou Earth System Science Interdisciplinary Center (ESSIC) University of Maryland, USA September 27, 2017, 9:30-9:50am

2 Outline Early RO Data Assimilation without Considering Cloud Contributions Positive Fractional N Bias of COSMIC Cloudy ROs Impact of Ice and Liquid Clouds on RO Observations An Empirical Temperature Retrieval Algorithm in Ice Clouds Recommendations for RO Data Assimilation 2

3 Local and Non-local RO Observation Operators 1. Local Refractivity Operator: p N = c 1 T + c pq 2 T 2 ( q) =106 (n 1) Zou, X., Y.-H. Kuo, and Y.-R. Guo, 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon. Wea. Rev., 123, Kuo, Y.-H., X. Zou, and W. Huang, 1997: The impact of GPS data on the prediction of an extratropical cyclone: An observing system simulation experiment, J. Dyn. Atmos. Ocean., 27, Non-local Bending Angle Operator: A Ray-tracing operator: d! 2 x ds = n n, dτ = 2 dτ n Zou, X., B. Wang, H. Liu, R. A. Anthes, T. Matsumura, and Y.-J. Zhu, 2000: Use of GPS/MET refraction angles in 3D variational analysis. Quart. J. Roy. Meteor. Soc., 126, Shao Hui, and X. Zou, 2002: On the observational weighting and its impact on GPS/MET bending angle assimilation. J. Geoph. Res., 107, ACL 19, Liu, H., and X. Zou, 2003: Improvements to a forward GPS raytracing model and their impacts on assimilation of bending angle, J. Geoph. Res., 108, D17, Zou, X., H. Liu, R. A. Anthes, H. Shao, J. C. Chang, and Y.-J. Zhu, 2004: Impact of CHAMP occultation observations on global analysis and forecasts in the absence of AMSU radiance data. J. of the Meteor. Soc. Japan, 82, Non-local Excess Phase Operator: ΔL N obs ds = s N LOC ds Shao, H., X. Zou, and G. A. Hajj, 2009: Test of a non-local excess phase delay operator for GPS RO data assimilation. J. Applied Remote Sensing, 3(1), , 16 pages. s Cloud contributions to refractivity N cloud contribution =1.45q liquid q ice were omitted. 3

4 Positive Fractional N Bias of COSMIC Cloudy ROs COSMIC Cloudy ROs Collocated with CloudSat during Positive Fractional N Bias (COSMIC ECMWF)/COSMIC Dc As Sc Ns Ci Ac Cu Deep convection (Dc) Altostratus (As) Height (km) Clear RO Cloudy points Height (km) Stratocumulus (Sc) Nimbostratus (Ns) Cirrus (Ci) Altocumulus (Ac) Cloudy RO Cumulus (Cu) Fractional N Bias (%) Data Count 4

5 Positive Fractional N Bias In Deep Convective Ice Clouds Deep Convective Ice Cloud Profiles collocated with COSMIC ROs during Height (km) IWC T=0 o C Height (km) N COSMIC N ECMWF N COSMIC (g m -3 )! Fractional N Diff (%) Ice Cloud Ice Cloud Clear Sky Clear Sky Frequency T COSMIC wet T ECMWF q COSMIC wet q ECMWF T COSMIC wet T ECMWF q COSMIC wet q ECMWF 5

6 Impact of Ice and Liquid Clouds on RO Observations Clouds containing liquid and ice can affect the propagation of the GPS RO signals through their scattering and absorption N = 77.6 P T P w T q liquid q ice T P P w Temperature (K) Pressure (hpa) Water vapor pressure (hpa) q liquid Liquid water content (g m -3 ) q ice Liquid water content (g m -3 ) The above expression can be derived using the Mie theory (assuming spherical particles) since the GPS wavelengths (~ 20 cm, 1.5 GHz frequency) are much larger than water droplets and ice particles. The refractivity due to scattering is N liquis = ( n liquid n a ) 10 6, n liquid - refractive index for liquid scatters, n air - refractive index for air, - refractive index for ice scatters n ice N ice = ( n ice n a ) 10 6, Gresh, D. L., 1990: Ph. D. Dissertation, Stanford University. 6

7 Impact of Ice and Liquid Clouds on RO Observations Deep Convective Ice Cloud Profiles collocated with COSMIC ROs during N Altocumulus cloud terms / N obs N Altostratus cloud terms / N obs N Stratocumulus Cloud terms / N obs N Nimbostratus Cloud terms / N obs Fractional N Diff (%) Altocumulus Altostratus Stratocumulus Nimbostratus!!!! LWC (g m -3 ) LWC (g m -3 ) LWC (g m -3 ) LWC (g m -3 ) Blue, red, flesh and green colored dots in the above figures represent the following quantity: N obs N calculated dry terms (T COSMIC wet,q COSMIC ) N calculated vapor terms (T COSMIC wet,q COSMIC CloudSat ) N cloud terms 1) Differences between N observations and RO retrievals increase linearly LWC at the same rate as the fractional contribution of clouds to N 2) Cloud contributions were put into dry and water vapor terms in RO wrongly retrievals to produce negatively biased T wet and positively biased q 3) Cloud contributions to total refraction must be included in both RO retrievals and data assimilation 7

8 Outline Positive Fractional N Bias of COSMIC Cloudy ROs Impact of Ice and Liquid Clouds on RO Observations o Theoretical Derivation of Cloud Terms o Magnitude of Cloud Terms Estimated from CloudSat An Ice Cloud Temperature Retrieval Algorithm o Algorithm Description for Cloudy Retrieval o Comparison between Cloudy and Wet Retrievals o Lapse Rate Characteristics in Ice Clouds Recommendations for RO Data Assimilation 8

9 An Ice Cloud Temperature Retrieval Algorithm Define N model : N model = (1 α)n clearsky + α N s cloudy, cloudy N saturate = 77.6 P T + e s (T ) q!#### "#### T$ 2 liquid q ice N clearsky α = a + bz where a and b are obtained by a linear regression to RH in clouds Solve for T cloud : J(T cloud m+1 ) = min N model obs ( (T m+1 ) N m+1 ) 2 T m+1 (N model -N obs ) 2 (N unit) A cloudy RO (51.02S, 66.27E) 1030 UTC 23 October 2008 Values of coefficient a and b with the same unit as relative humidity (%). Cloud As Ci Dc Ns a b Sample T cloud Temperature ( o C) Height (km) Total numbers of COSMIC GPS RO cloudy profiles collocated with CloudSat measured pure ice clouds of four cloud types (e.g., As, Ci, Dc and Ns) during a seven-year period from 2007 to

10 COSMIC Cloudy ROs Collocated with CloudSat Ice Clouds during Cirrus Altostratus Nimbostratus & Deep Convection IWC in Dc Vertical distributions of IWC (unit: g m -3 ) within the pure ice clouds of Dc type from the cloud top to cloud base for a total of 92 cloudy profiles from the equator to higher latitudes in both hemispheres. (kg m -3 ) 10

11 Vertical Variations of the Lapse Rate in Ice Clouds Color shadings indicate the total number of COSMIC RO cloudy ROs within each of the box divided at the 0.2 o C/km lapse rate and 0.4 km altitude intervals. Averaged cloud top (open circle) and -20 o C height (solid dot) and their standard deviation (vertical line) for single layer clouds of As, Ns, Dc and Ci. 11

12 Comparison of Lapse Rate between ROs and Radiosondes Geographical distribution of radiosonde stations and the number of radiosonde profiles (colors) that were collocated with CloudSat observed pure ice clouds during The collocation criteria are a temporal difference of less than 1 hour and a spatial separation of less than 100 km. COSMIC RO Radiodone Mean Lapse Rate Altostratus Nimbostratus Deep Convection Cirrus Lapse Rate Standard Deviation Altostratus Nimbostratus Deep Convection Cirrus 12

13 Recommendations for RO Data Assimilation 1) Clear-sky RO Data Assimilation o Omit Cloud Terms in RO Observation Operators o Incorporate a Bias Correction for Assimilating Cloudy ROs 2) Cloudy RO Data Assimilation o Include Cloud Terms in RO Observation Operators o Assimilate Cloudy ROs and Cloudy Radiances Together NOAA-18 LWP ( ) CloudSat RO tangent line (600 km) (kg m -3 ) A GPS RO profile, the cross-section of CPR LWC (white shading, unit: kg m -3 ) along a portion of CloudSat track that passed through the observed GPS RO location, and the horizontal distribution of LWP (color shading, unit: kg m -2 ) from NOAA-18 AMSU-A. 13

14 More details can be found in the following articles: Lin, L., X. Zou, R. Anthes and Y.-H. Kuo, 2010: COSMIC GPS Cloudy Profiles. Mon. Wea. Rev., 138, doi: /2009MWR Yang, S. and X. Zou, 2012: Assessments of cloud liquid water contributions to GPS RO refractivity using measurements from COSMIC and CloudSat. J. Geophy. Res., 117, D doi: /2011jd Zou, X., S. Yang, and P. Ray, 2012: Impacts of ice clouds on GPS radio occultation measurements. J. Atmos. Sci., 67(12), Yang, S. and X. Zou, 2013: Temperature profiles and lapse rate climatology in altostratus and nimbostratus clouds derived from GPS RO data. J. Climate, 26, Yang, S. and X. Zou, 2017: Dependence of positive N-bias of GPS RO cloudy profiles on cloud fraction along GPS RO limb tracks. GPS Solution, 21, Yang S. and X. Zou, 2017: Lapse rate characteristics in ice clouds inferred from GPS RO and CloudSat observations. Atmos. Res., 197,