Quantification of atmospheric temperature and water vapour are critical for assessing and improvement of numerical weather and climate prediction models ([1
] and references therein). The initialization process for these models demand the use of denser and homogeneous satellite radiance which must be corrected for cloud contamination. This radiance correction allows for the effective and efficient retrieval of atmospheric profiles such as water vapour, temperature, ozone and other trace gases. Retrieval skill is dependent on sensor accuracy, the atmospheric transmittance functions, cloud clearing and inversion algorithms [2
]. The availability and accuracy of observational calibration/validation data, especially observations from radiosondes is critical to the development of robust atmospheric profile retrieval algorithms and products. Water vapour is a particularly important because its presence in the form of clouds can induce either a positive or negative temperature feedback in the climate system based on height of occurrence (e.g., Mears et al. [3
]). Therefore, understanding and modeling the spatiotemporal variability of atmospheric moisture is essential to weather and climate prediction.
Radiosonde observations (RAOB) offer an adequate platform for the monitoring of the vertical profile of water vapour, temperature, wind, and geopotential height. When assimilated into weather forecast models, RAOBs can enhance the prediction of convective storm evolution in terms of initiation, propagation and decay [4
]. However the spatial distribution of radiosondes are limited with few launches in the equatorial tropical region that is characterized by strong convective activities [6
]. The radiosonde has the advantage of being highly accurate with high vertical resolution [9
], but the frequency of sonde launches in time and space is low due to the large operational cost [9
]. The African Monsoon Multidisciplinary Analysis (AMMA) [11
] and Dynamics-aerosol-chemistry-cloud interactions in West Africa (DACCIWA) [12
] campaigns in 2006 and 2016 respectively mark years in which RAOBs are available for West Africa.
With advances in remote sensing, sounders aboard satellites offer alternate sources for the acquisition of RAOB-like vertical profiles. The majority of these validation studies have focused on inter-comparing the retrievals from satellite-based platforms with corresponding collocated radiosonde measurements. A well-known sensor is the Atmospheric Infrared Sounder (AIRS) aboard NASA’s Earth Observing System (EOS) Aqua satellite [13
]. AIRS was constructed to provide atmospheric temperature profiles to a root mean square difference (RMSD) of 1 K for every 1 km tropospheric layer and 1 K for every 4 km stratospheric layer up to an altitude of 40 km [14
]. The corresponding humidity RMSD of the sensor is of order 20% in 2 km layers in the lower troposphere and approximately 50% in the upper troposphere [15
]. These error estimates are considered to be applicable for scenes of up to 80% effective cloud cover [17
]. According to McMillin et al. [18
] (and see references therein), the AIRS instrument has provided a set of unique datasets by which to validate climate and weather models and analyse the global distribution of water vapour and ice supersaturation. AIRS temperature and water vapour datasets have also been evaluated to improve parameterisation of sub-grid scale models [19
] and to understand regional climatology, including land-atmosphere coupling [20
Currently, there is a rigorous ongoing AIRS validation efforts using various ground truths across the world, Iran [10
], India [22
], Antarctica [24
] and continental United States [18
]. The studies also provide information on performance improvements of recent AIRS version releases over earlier releases [25
]. Most of these studies observed a good agreement between AIRS and RAOB profiles with an overall accuracy within mission-specified accuracy bounds [22
]. Bayat and Maleki [10
] validated AIRS derived precipitable water vapour profiles with a ground-based sun photometer measurements and obtained an acceptable agreement with a 93% coefficient of determination. Seasonal analysis over Iran showed higher dry biases of the precipitable water vapour during spring with lower values in the winter. Over India, Singh et al. [23
] found that AIRS and the Indian National Satellite (INSAT-3D) agree comparatively well with RAOB observations at the lower and upper troposphere but quickly degrades in the middle troposphere probably due to improper bias correction coefficients used for brightness temperature. Their findings observed the influence of surface emissivity on the AIRS profile retrievals which resulted in larger errors over land and in dry atmosphere. Divakarla et al. [2
] also observed a decreased performance of AIRS temperature and water vapour profiles relative to the Advanced TIROS Operational Vertical Sounder (ATOVS) [27
] retrievals and the National Center for Environmental Prediction Global Forecasting System (NCEP_GFS) and European Center for Medium Range Forecast (ECMWF) forecast profiles over land measurements which exhibited a seasonal and annual variability that correlates with changes in CO
concentrations. However, the overall agreement was satisfactory for both land and sea surface categories. Furthermore, AIRS was merged with the Microwave Limb Sounder (MLS) temperature and water vapour records to successfully study the inter-annual variability of these parameters over tropical Pacific [28
]. Their findings revealed the spatial and seasonal distribution of temperature and humidity to be located over the deep convection zone of the tropical western Pacific whereas subsidence dominates at the tropical central Pacific. Based on these datasets, the authors [28
] were able to observe and link the inter-annual variability of major tele-connections such as the El Nino Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO). To date, there have been no dedicated analyses of AIRS retrieval performance over West Africa. For example, Ferguson and Wood [21
] could only utilise four radiosonde observations stations from the AMMA project into the validation section (AIRS versus radiosonde) of their land-atmosphere coupling study.
Our study inter-compares AIRS vertical profiles of temperature and relative humidity with AMMA and DACCIWA radiosonde observations at some selected West African stations for which there are sufficient data matchups. For context, AIRS retrieval skill is compared against that of NCEP_R2 at the same sites. Notably, NCEP-R2 does not assimilate AIRS, as do more modern atmospheric reanalyses, but does assimilate RAOBs. Results from this study will give a first hand confidence in the use of the AIRS datasets for the profiling of temperature and relative humidity that exist in a pre-convective environment for thunderstorm initiation. It is also in accordance with the Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project which seeks to develop a sustainable research capability in tropical weather forecasting. The remaining part of the paper is structured into three sections which includes the methodology in Section 2
, results and discussions in Section 3
and finally the conclusion in Section 4
Determination of a pre-convective environment for thunderstorm formation requires a long time-series of sounding data. Radiosonde observation offer the most accurate vertical profiles of temperature and relative humidity. However, these observations are scarce in West Africa and hence there is the need to rely on suitable satellite products for convection assessment. The Atmospheric InfraRed Sounder on-board the AQUA satellite provides atmospheric sounding information twice daily, which may be used as a reliable substitute for RAOB in sparse regions upon rigorous validation. The study assessed the performance of the AIRS IR-Only level 3 standard retrieval version 6 and for context, NCEP_R2 vertical temperature and relative humidity profiles for some selected AMMA and DACCIWA radiosonde observation stations in West Africa within spatio-temporal collocation radius of 100 km and ±3 h for AIRS and NCEP_R2. The performance of AIRS vertical profiles for diurnal, seasonal, cloud and cloud-free analyses as well as with collocated NCEP_R2 profiles were assessed. Finally seasonal variation of three thunderstorm convective indices (K-Index, TT index and HI) for each station was computed and compared for RAOB, AIRS and NCEP_R2.
The diurnal temperature profile reveals lower biases however with corresponding higher RMSD above the AIRS mission goal of ±1 K. AIRS temperature RMSD show higher values at the coast as compared to inland regions, possibly due to complications in surface emissivity, skin temperature and the diurnal sea and land breeze effect which is strongest along the coast. The reverse of the temperature RMSD however is observed to occur at night. The relative humidity on the other hand, was found to be more accurate for the descending pass than ascending for all zones with the coastal stations dominating in all passes. On the seasonal timescale, the temperature bias for the dry season is pre-dominantly cold. The corresponding RMSD were also higher and deviated towards the inland wet season profile. The coastal dry season was the least deviated, albeit, all zonal deviations were higher (≈1.0–5 K). Inland wet season RH profile was the most biased (cold) whereas the RMSD showed satisfactory performance at all tropospheric levels for all zones and seasons. Cloudy conditions were found to have no significant effect on the RH retrievals by AIRS as the bias and RMSD between cloudy and non-cloudy days were found to have marginal differences and both achieving the AIRS accuracy goal of <20%, and 50% for lower and upper troposphere, respectively. The temperature retrievals however are better on cloud-free than cloudy days. Comparison of the temperature and RH retrievals of AIRS with NCEP_R2 reveal AIRS to be a better substitute for RAOB vertical profiles at the coast and inland. Finally, the seasonal derived thunderstorm indices for AIRS and NCEP_R2 showed that both datasets can be utilised for the occurrence and non-occurrence of thunderstorms in the wet and dry seasons though NCEP_R2 generally over-estimates the thunderstorm probability. Comparing the derived indices of AIRS and NCEP_R2 with RAOB indices at the seven stations also show a higher agreement for all seasons.
In general, the performance of AIRS at these West African stations has been satisfactory for the temperature (although with slight over-estimations) and the RH. Based on the performance of AIRS for the derivation of thunderstorm convective instability indices, it is proposed to be used further for determining the probability of convection initiation over West Africa under the GCRF African SWIFT project by focusing on the statistical analysis of thunderstorm convective indices over the region.